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Summer 8-2018

The Influence of an Electronic Attendance Monitoring System on The Influence of an Electronic Attendance Monitoring System on

Undergraduate Academic Success Undergraduate Academic Success

Charles Childress University of Southern Mississippi

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The Influence of an Electronic Attendance Monitoring System

on Undergraduate Academic Success

by

Charles Felix Childress, III

A Dissertation

Submitted to the Graduate School,

the College of Science and Technology

and the Department of Human Capital Development

at The University of Southern Mississippi

in Partial Fulfillment of the Requirements

for the Degree of Doctor of Philosophy

Approved by:

Dr. H. Quincy Brown, Committee Chair

Dr. Heather M. Annulis

Dr. Cyndi H. Gaudet

Dr. Dale L. Lunsford

Dr. Chad R. Miller

____________________ ____________________ ____________________

Dr. H. Quincy Brown

Committee Chair

Dr. Cyndi H. Gaudet

Department Chair

Dr. Karen S. Coats

Dean of the Graduate School

August 2018

COPYRIGHT BY

Charles Felix Childress, III

2018

Published by the Graduate School

iii

ABSTRACT

Investing in human capital development increases education levels, workplace

skills, and boost individual abilities. Undergraduate students who attend class and

perform well are more likely to get jobs, due to their development of workplace skills.

State governments, as the funding bodies for public universities, are finding it beneficial

to increase the number of college graduates because a citizenry that is prepared for the

job market is ultimately good for the state. States recognize that an increase in education

can produce job opportunities for citizens. University administrators can employ tactics

to increase graduation rates, one of which is monitoring students’ class attendance.

This study uses a quasi-experimental design to analyze the influence of an

electronic attendance monitoring system on undergraduate academic success. The

researcher uses point-biserial and logistic regression to analyze archival data. Through

this analysis of the current study, three findings were present: (a) an electronic attendance

monitoring system increased academic success for students, (b) the presence of a positive

relationship between electronic attendance monitoring and academic success, and (c)

different literature-based demographics effect academic success of students depending on

the course. Finally, the results show that attendance increases student academic success

and implementing an electronic attendance monitoring system provides attendance

accountability in the classroom.

iv

ACKNOWLEDGMENTS

I realize that I am lucky! I have had the support and encouragement of many

friends, family members, colleagues, mentors, students in the program, and

undergraduate students at Southern Miss, even from those who did not fully understand

what I was doing. Special thanks to my Mom, Dad, Katie, Martin, Johan, Uncle Jim,

Aunt Phe, Uncle Steve, Aunt He, Ellen, and Lizzie thanks for all your support. Thank

you to my many relatives for all your encouragement. Also, thanks to the many people in

my life I consider close family without being blood related; you have truly made an

impact on my life. Thanks to my grandparents (and others) for instilling pride, morals,

and values in my life. While I name additional individuals, I also realize I will forget

someone who has played a role in the completion of my degree. Please know that no

matter your role, large or small, I am appreciative of your unending support.

The encouragement that everyone has shown me during the process has kept me

focused on the project. As the saying goes, “Slow and steady wins the race.” To my

family, thank you for always trying to understand that this “little paper” meant so much

and that the countless hours spent typing were not just playing games. To John Hubbard

and Steve Ellis, THANK YOU! Your steadfast friendship and mentorship throughout this

journey has meant the world to me. From the highest of highs to the lowest of lows, you

were there for me and I will never forget it. To my other program colleagues, Donna,

Robert, Deano, Steve, Ellie, Kristy, and Lisa, thanks for all the encouragement and

friendship along the way. To the best class project partner, Tonya, thanks for always

having just as much fun as I did, and for giving me another nickname, Chris.

v

To Megan Wilkinson and Johnell Goins, thank you for everything. Thanks for

always understanding that I was crazy to spend late nights in the library and come back at

8am to do a full workday. You made coming to the office fun and enjoyable and I know

that together we made a difference that very few will ever begin to understand. To Gary

Kimble, my first supervisor and mentor, thank you for encouraging me to start this

journey, understanding my commitment to the cause, allowing me to take classes in a

non-traditional format, and supporting me every step of the way. Your mentorship means

the world to me! To Casey Thomas, thanks for all your help in the Research Support

Center and with helping me through the myriad of challenges that occurred throughout

the journey. Additionally, I would like to thank other colleagues and mentors (Sid

Gonsoulin, Eddie Holloway, Mike Lopinto, Jay Dean, Maryann Kyle, Toni Stringer,

Justin Long, Rusty Anderson, April Woodall, Karen Reidenbach, and Kelly James-Penot)

for all the help, support, and encouragement throughout the journey. Additionally, I am

fortunate for the many colleagues that became friends during my time at Southern Miss

and will treasure our experiences together, forever.

I would like to acknowledge the faculty, staff, and students of The University of

Southern Mississippi’s Department of Human Capital Development at the Gulf Park

campus. The supportive structure, learning atmosphere, challenging curriculum, and

lasting friendships experienced during the past few years made the process enjoyable and

my goals achievable. To the faculty in the Human Capital Development Program (Dr.

Cyndi Gaudet, Dr. Heather Annulis, Dr. Dale Lunsford, Dr. Patti Phillips, Dr. Jack

Phillips, Dr. Sandra Dugas, and Dr. John Kmiec), thank you for providing a wonderful

education and supportive educational environment. To my dissertation chair, Dr. H.

vi

Quincy Brown, what can I say. Thanks for putting up with me, this young, energetic,

enthusiastic, inquisitive, semi-stubborn pupil. They say you become close with your

dissertation chair, and I was lucky to have someone like you, who understood me and

what I wanted to do every step of the way. I am proud to be one of your graduates! Dr.

Gaudet, thanks for encouraging me when I needed it the most, which happened to be

during my very first class. Dr. Annulis, thanks for “putting your money on me” and

encouraging me to finish. Suzy Robinson, thanks for giving me the final kick in the rear

to complete my application for admittance, a decision I will never forget.

To my friends, thanks for understanding that throughout this journey, sometimes I

just could not attend a function or spend time with you because I needed to finish this

opportunity and education. Make no mistake, your friendship through the years means

the world to me. Special thanks to two truly dear friends, Elizabeth Bush Foreman and

Angela Davis-Morris, who helped keep me sane during the entire process. Thanks to my

friends at Crescent City Grill and especially Jarred Patterson and Dusty Frierson for

giving me a new hope. To my new family at Cintas, I am looking forward to our journey

together. To my Sigma Phi Epsilon fraternity brothers, thanks for always encouraging

me to take a break and live life. To my marathon running best friends, Dustin Ellzey,

Chris Thompson, and Phillip Parnell, thanks for always making me unplug to enjoy

friendship with all of you as we travelled the country. To the students I have worked

with through the years, especially those who held leadership positions, thanks for making

“work” fun.

Now that the journey is complete, Let’s Party!

vii

DEDICATION

Throughout the journey to pursue my doctoral degree, I have had the support of

many friends, family members, colleagues, mentors, students in the program, and

undergraduate students I have worked with at Southern Miss. I have been fortunate to

share the journey with many family and friends. I dedicate this research to my Mom and

Dad, sister Katie, Brother-in-law Martin, and my family. Also, I want this research to

affect all of my godchildren. Further, I dedicate my research study to anyone the

research may affect in a positive way.

viii

TABLE OF CONTENTS

ABSTRACT ................................................................................................................... iii

ACKNOWLEDGMENTS ................................................................................................. iv

DEDICATION .................................................................................................................. vii

TABLE OF CONTENTS ................................................................................................. viii

LIST OF TABLES ........................................................................................................... xiv

LIST OF FIGURES ......................................................................................................... xvi

CHAPTER I – INTRODUCTION ...................................................................................... 1

Background of the Study ................................................................................................ 2

Problem Statement .......................................................................................................... 4

Statement of Purpose ...................................................................................................... 5

Research Questions and Objectives ................................................................................ 5

Conceptual Framework ................................................................................................... 6

Significance of the Study ................................................................................................ 9

Definition of Terms....................................................................................................... 10

Assumptions of the Study ............................................................................................. 11

Delimitations of the Study ............................................................................................ 12

Chapter Summary ......................................................................................................... 12

CHAPTER II – LITERATURE REVIEW ....................................................................... 14

Defining Human Capital Development ........................................................................ 14

ix

Psychological Domain .............................................................................................. 16

Economic Domain .................................................................................................... 17

Systems Domain ....................................................................................................... 19

Connecting Human Capital Development and Today’s Higher Education .................. 20

Student Development and Human Capital Development ............................................. 22

Social Interaction ...................................................................................................... 23

Workplace Skill Development .................................................................................. 24

Connecting Attendance, Student Development, Academic Success and Human Capital

Development ................................................................................................................. 24

Human Capital Development and Mississippi .............................................................. 26

Human Capital Development Today ............................................................................ 30

Human Performance Interventions and Behavior Change ............................................ 32

Human Performance Interventions ........................................................................... 33

Attendance Monitoring Systems ............................................................................... 34

Implementing Behavior Change Practices ................................................................ 35

Transtheoretical Model of Change........................................................................ 37

Theory of Planned Behavior ................................................................................. 38

Demographics, College Success, and Undergraduate Student Attendance .................. 39

Gender ....................................................................................................................... 41

Local Residency ........................................................................................................ 41

x

State Residency ......................................................................................................... 42

Greek Life Organization Affiliation ......................................................................... 43

Admission Type ........................................................................................................ 44

Cumulative Grade Point Average ............................................................................. 45

Age ............................................................................................................................ 46

Undergraduate Year Classification ........................................................................... 46

Ethnicity .................................................................................................................... 47

Current Enrollment Status......................................................................................... 48

University Designation ............................................................................................. 49

Chapter Summary ......................................................................................................... 50

CHAPTER III – RESEARCH DESIGN AND METHODOLOGY ................................. 52

Research Objectives ...................................................................................................... 53

Research Design ............................................................................................................ 53

Population and Sample ................................................................................................. 56

Previous Academic Success of Sample ........................................................................ 56

Sampling Process .......................................................................................................... 58

Instrumentation ............................................................................................................. 59

Data Collection ............................................................................................................. 60

Institutional Review Board ........................................................................................... 61

Data Request and Confidentiality of Data .................................................................... 62

xi

Data Analysis ................................................................................................................ 63

Data Analysis Plan ........................................................................................................ 64

Validity and Reliability ................................................................................................. 68

Chapter Summary ......................................................................................................... 70

CHAPTER IV – RESULTS .............................................................................................. 71

Research Objective 1 – Describing demographics, attendance, and academic success

rates ............................................................................................................................... 71

Demographic Characteristics ................................................................................ 71

Electronic Attendance Monitoring System Descriptive Results ........................... 78

Academic Success Rates ....................................................................................... 83

Research Objective 2 – Difference in academic success rates when using an electronic

attendance monitoring system....................................................................................... 90

Research Objective 3 – Relationship between attendance rates and academic success 94

Research Objective 4 – Relationship between demographic factors and attendance rates

on academic success rates ............................................................................................. 96

Introduction to Research Objective 4 ....................................................................... 97

All Courses Combined .............................................................................................. 98

History 101 Analysis............................................................................................... 101

History 102 Analysis............................................................................................... 104

Math 102 Analysis .................................................................................................. 106

xii

Chapter Summary ....................................................................................................... 108

CHAPTER V – CONCLUSIONS .................................................................................. 110

Findings, Conclusions, and Recommendations .......................................................... 110

Finding 1: Academic success rates increased with attendance monitoring ............ 111

Conclusions ............................................................................................................. 111

Recommendations ................................................................................................... 112

Finding 2: Academic success increases as attendance increases ............................ 113

Conclusions ............................................................................................................. 113

Recommendations ................................................................................................... 113

Finding 3: Courses are unique; Demographics correlate with academic success

differently in combined or individual data sets, while attendance remains important

throughout ............................................................................................................... 114

Conclusions ............................................................................................................. 115

Recommendations ................................................................................................... 117

Implications for Human Capital Development ........................................................... 118

Limitations .................................................................................................................. 119

Recommendations for Further Research ..................................................................... 120

Discussion ................................................................................................................... 121

Summary ..................................................................................................................... 122

APPENDIX A – Use of table permission ....................................................................... 124

xiii

APPENDIX B – Institutional research pre-approval ...................................................... 125

APPENDIX C – Institutional review board approval letter ............................................ 126

APPENDIX D – Attendance rates for all courses ........................................................... 127

APPENDIX E – Academic success outcomes ................................................................ 128

APPENDIX F – Electronic attendance monitoring system gender comparison results . 130

APPENDIX G – Electronic attendance monitoring system local residence comparison 131

APPENDIX H – Electronic attendance monitoring system state residence comparison 132

APPENDIX I – Electronic attendance monitoring system Greek life affiliation

comparison ...................................................................................................................... 133

APPENDIX J – Electronic attendance monitoring system admit type comparison ....... 134

APPENDIX K – Electronic attendance monitoring system cumulative GPA comparison

......................................................................................................................................... 135

APPENDIX L – Electronic attendance monitoring system age comparison .................. 136

APPENDIX M – Electronic attendance monitoring system classification comparison . 137

APPENDIX N – Electronic attendance monitoring system ethnicity comparison ......... 138

APPENDIX O – Electronic attendance monitoring system enrollment status comparison

......................................................................................................................................... 139

REFERENCES ............................................................................................................... 140

xiv

LIST OF TABLES

Table 1 Fall 2005 Freshman Cohort Student Graduation Rates ....................................... 39

Table 2 Academic Success in Electronic Attendance Monitoring System Participating

Courses .............................................................................................................................. 57

Table 3 Electronic Attendance Monitoring System Participating Courses ...................... 58

Table 4 Data Analysis Timetable ...................................................................................... 63

Table 5 Data Analysis Plan ............................................................................................... 68

Table 6 Group Demographic Characteristic Comparison ................................................. 72

Table 7 Breakdown of Students in Participating and Non-Participating Courses ............ 83

Table 8 Academic Success in Participating Courses: Monitored vs. Non-Monitored ..... 85

Table 9 Academic Success Course Data Comparison: History 101 ................................. 86

Table 10 Academic Success Course Data Comparison: History 102 ............................... 88

Table 11 Academic Success Course Data Comparison: Math 102 ................................... 89

Table 12 Attendance Monitoring Versus Non-Attendance Monitoring: History 101 ...... 91

Table 13 Attendance Monitoring Versus Non-Attendance Monitoring: History 102 ...... 92

Table 14 Attendance Monitoring Versus Non-Attendance Monitoring: Math 102 .......... 93

Table 15 Correlation Between Attendance Rates and Academic Success ....................... 95

Table 16 Logistic Regression Identification Coding Used in SPSS ................................. 97

Table 17 Demographic and Attendance Relationship on Academic Success – All Courses

Combined .......................................................................................................................... 99

Table 18 Demographic and Attendance Relationship on Academic Success – History 101

......................................................................................................................................... 102

xv

Table 19 Demographic and Attendance Relationship on Academic Success – History 102

......................................................................................................................................... 104

Table 20 Demographic and Attendance Relationship on Academic Success – Math 102

......................................................................................................................................... 107

Table A1 Attendance Rates for All Courses Adapted from Figure 6 ............................. 127

Table A2 History 101 Academic Success Outcomes ..................................................... 128

Table A3 History 102 Academic Success Outcomes ..................................................... 128

Table A4 Math 102 Academic Success Outcomes ......................................................... 129

Table A5 Comparison of Gender Results ....................................................................... 130

Table A6 Comparison of Local Residence Results ........................................................ 131

Table A7 Comparison of State Residence Results ......................................................... 132

Table A8 Comparison of Greek Life Affiliation Results ................................................ 133

Table A9 Comparison of Admission Type Results ........................................................ 134

Table A10 Comparison of Cumulative GPA Results ..................................................... 135

Table A11 Comparison of Age Results .......................................................................... 136

Table A12 Comparison of Classification Results ........................................................... 137

Table A13 Comparison of Ethnicity Results .................................................................. 138

Table A14 Comparison of Enrollment Status Results .................................................... 139

xvi

LIST OF FIGURES

Figure 1. Conceptual Framework ....................................................................................... 8

Figure 2. Educational attainment of working adults aged 25 to 64 .................................. 28

Figure 3. Median annual wages for employed workers aged 25 to 64 by level of

education ........................................................................................................................... 31

Figure 4. Research design of the current research project................................................ 55

Figure 5. Combined Classroom Attendance Data ............................................................ 79

Figure 6. Individual Course Attendance Monitoring Data ............................................... 81

1

CHAPTER I – INTRODUCTION

Students who enroll in a college or university make a personal decision to

enhance their knowledge in a field of study (Pascarella & Terenzini, 1991; Robertson,

Hurst, Williams, & Kieth, 2017). Simply stated, undergraduate students who attend class

and perform well are more likely to get jobs following graduation due to their

development of workplace skills. Multiple factors determine student academic success,

including the personal level of engagement and the social support received (Mackinnon,

2012; Tinto, 1993, 1985, 2006). Nationally, only 60% of students who enroll as first-

time, full-time freshmen complete a degree within six years of initial acceptance (U.S.

Department of Education, National Center for Education Statistics, 2016). From another

perspective, 40% of students who enter college do not complete a degree program (U.S.

Department of Education, National Center for Education Statistics, 2016). This statistic

offers an impetus for change. Universities across the country are attempting to create

environments that focus on promoting academic success and skill building to increase

student involvement, attendance, and academic success (Bailey & Morais, 2005).

Implementing engagement-based initiatives has shown to provide the highest results to

influence academic success of students (Fike & Fike, 2008; Tinto, 2006).

Research demonstrates a positive relationship between the frequency of

class/lecture attendance and students’ academic success (Moore, 2003). Crede, Roch,

and Kieszczynka (2010) report that student attendance is a better indicator of academic

success than standardized test scores or high school grade point averages. Additional

research shows that students involved in lectures achieve higher grades than students who

do not attend class regularly (Benzing & Christ, 1997; Bligh, 1998; Markham, Jone,

2

Hughes, & Sutcliffe, 1998). Students who regularly attend classes are able to connect

with faculty (Kanfer & Ackerman, 1989). Specifically, Kanfer and Ackerman’s (1989)

research suggests that student engagement during the collegiate experience can change

motivation levels of students, which tends to boost students’ academic success rates.

The present study examines whether the use of an electronic attendance

monitoring system impacts undergraduate student academic success. Additionally, this

study is an expansion on previous research as it determines the influence of multiple

literature-based demographic factors affecting attendance and academic success

concurrently. Chapter I introduces the study and contains the background of the study,

problem, purpose, research objectives, conceptual framework, significance, assumptions,

and delimitations associated with the study. The chapter ends with a summary of

presented materials. The background of the study provides information that describes the

problem and purpose of the study.

Background of the Study

In 1990, Congress passed the Student Right-to-Know Act, requiring all colleges

and universities eligible for federal funding to report graduation rates of students who

begin each year together in a cohort (National Center for Education Statistics, 2017).

Data originating from the National Center for Education Statistics (2017) highlights the

problem of non-completion across the country. Many reasons exist to preclude someone

from completing their degree program. One reason for non-completion is the lack of

academic success in attempted courses (Noel, Levitz, & Saluri, 1985). Lack of

attendance may cause low levels of academic success in the classroom (Romer, 1993).

3

Frequently, the lack of attendance points to the fact that attendance is not tracked in

college classrooms (Dicle & Levendis, 2013; Newsom, 2016; O’Connor, 2010).

Recruitment and retention play a direct role in a university’s enrollment (Noel et

al., 1985). Recruitment of a prospective student begins as soon as the individual interacts

with the college. Enrollment at a university begins when a prospective student registers

for college classes after completing high school, attending junior or community college,

or returning to college as a member of the workforce (Noel et al., 1985). Retention

focuses on keeping students enrolled in university courses until degree completion so

they can receive the necessary training and education for success in the workplace (Noel

et al., 1985). Retaining students already connected to the institution is financially more

feasible than attracting new students through recruitment efforts (Noel et al., 1985). By

focusing on academic success, especially during the first year of enrollment, universities

can positively impact retention and graduation rates (Fike & Fike, 2008; Tinto, 2006).

Nationally, the retention rate for open-admission colleges and universities hovered

around 62% for the fall 2014 cohort. This rate describes individuals who enrolled in the

fall 2014 semester and returned to the same institution in the fall 2015 semester (National

Center for Education Statistics, 2017).

Successful completion of courses plays a significant role in determining college

student retention rates (Slanger, Berg, Fisk, & Hanson, 2015). Effective academic

success strategies focus not only on academic endeavors but also on activities to promote

student academic growth through interactions both within and outside of the classroom

(Roberts & Styron, 2010). Attendance accounts for a 31% increase in academic

performance (Romer, 1993). Student engagement and involvement in campus activities

4

affect academic success and student retention (Tinto, 1975, 2006). Specifically, colleges

can support academic success by encouraging interactions that promote classroom

attendance (Benzing & Christ, 1997; Bligh, 1998; Markham et al., 1998; Tinto, 1993).

One way to encourage class attendance is by using an electronic attendance monitoring

system.

Problem Statement

When students are successful in college courses, they develop workplace skills to

help fill the current skills gap in the workforce (Kaplan, 2017). Ideally, undergraduate

students complete their degree programs in a timely fashion, while developing workplace

skills that contribute to the human capital needs of society (Blackwell, Bowes, Harvey,

Hesketh, & Knight, 2001; Noel et al., 1985; Pascarella & Terenzini, 1991; Robertson et

al., 2017). However, the reality is organizations, researchers, and policymakers believe

employees’ workplace skills fail to match the requirements and needs for existing jobs

(Altonji, Blom, & Meghir, 2012; Handel, 2003; Swanson & Holton, 2009). One factor

contributing to this skill gap is the low level of undergraduate degree completion (Altonji

et al., 2012; Swanson & Holton, 2009). On a national level, four out of ten

undergraduate college students leave college without graduating, establishing a skills gap

in human capital needs in the workforce (National Center for Education Statistics, 2017;

White House, Office of the Press Secretary, 2009). In the state of Mississippi, five out of

ten undergraduate college students leave college without achieving their degrees

(Institutions of Higher Learning Board, State of Mississippi, 2013). Generally, poor

college student attendance is due to a lack of accountability, which enables a low rate of

attendance, while also limiting interactions between faculty and students (Jones,

5

Crandall, Vogler, & Robinson, 2013). Automated accountability tools can improve

attendance, resulting in increased interaction between faculty and students, greater

academic success, and increased graduation rates (Borland & Howsen, 1998). When

students fail to complete their degrees, thus failing to develop workplace skills, the

resulting lack of a sustainable workforce impacts an organization or state’s competitive

advantage.

Statement of Purpose

The purpose of the present study is to determine the influence of an electronic

attendance monitoring system on undergraduate student success. Specifically, this study

assesses if an electronic attendance monitoring system affects student academic success.

Low class or lecture attendance among undergraduate students remains an issue in the

collegiate setting (Lopez-Bonilla & Lopez-Bonilla, 2015). Much of the limited research

investigating the link between an electronic attendance monitoring system and student

academic success examines only one demographic variable (eg. sex, ethnicity, age, or

campus residency). The present study examines the connection of multiple demographic

factors simultaneously, showing how an electronic attendance monitoring system

influences undergraduate student success, regardless of other demographic factors.

Research Questions and Objectives

The following research question guides the present study. Does the electronic

attendance monitoring system have an effect on student success when considering

literature-based demographics? The following research objectives guide the study:

RO1 - Describe the literature-based undergraduate demographic factors,

attendance rates, and academic success rates of the student sample.

6

RO2 - Compare academic success rates of students in the sections of courses

using the electronic attendance monitoring system in the Spring 2015 Semester with the

sections of courses that did not use the system.

RO3 - Determine the relationship between undergraduate attendance rates and

academic success in courses using an electronic attendance monitoring system.

RO4 - Determine the relationship between the literature-based undergraduate

demographic factors and attendance rates on student academic success in courses using

an electronic attendance monitoring system.

Conceptual Framework

A conceptual framework, shown in Figure 1, provides a graphic illustration of

theories, connections, ideas, and interactions supporting the objectives of the present

study (C. Roberts, 2010). The present study focuses on two major areas affecting student

academic success in at the university level: student development and human capital

development. Rodgers (1990) defines student development as “the ways that a student

grows, progresses, or increases developmental capabilities because of enrollment in an

institution of higher education” (p. 27). While student development focuses on the

educational environment, human capital development focuses on the improvement or

performance of an individual, a team, or an organization through the development of

knowledge, skills, and abilities (Swanson & Holton, 2009).

Student development in college is inseparable from the level of faculty-student

interaction. Astin’s (1975) Theory of Involvement contends that academic success is

based on the quantity and quality of interactions between faculty and students. The

involvement theory describes how student involvement, and interactions between faculty

7

and students during college, enhances cognitive and skill development, both of which

improve effective learning. According to Astin (1984), “Student involvement equates to

the amount of physical and psychological energy that the student devotes to the academic

experience” (p. 297). Involvement in the college classroom environment translates to

increased academic success, which serves as a significant source of skill development,

learning and student development (Astin, 1984).

The development a student receives during a college education increases the

human capital of society by building workplace skills that benefit the student later in life

(Dychtwald, Erickson, & Morison, 2006). These workplace skills relate to three areas

that pertain to human capital development: economic, psychological, and systems

focused on improving undergraduate student attendance rates and academic success

(Becker, 1993; Crede et al., 2010).

Figure 1 presents the conceptual framework for the present study, showing the

interaction of the three core research areas: attendance, academic success, and

attendance-based demographics. The conceptual framework shows the student point of

entry and separates courses according to use of the electronic attendance monitoring

system.

8

Figure 1. Conceptual Framework

Human Capital Development

Theories

Argyris & Schon (1982)

Becker (1993)

Florida (2004)

Handel (2003)

McLean & McLean (2001)

Schultz (1961)

Swanson & Holton (2009)

Student Development

Theories

Astin (1975, 1984, 1993)

Chickering & McCormack (1973)

Pascarella & Terenzini (1991, 2005)

Tinto (1975, 1985, 1993, 2006)

9

Significance of the Study

The present study has relevance to three different audiences: the State, the

university, and the individual. First, the study may identify factors to improve academic

success throughout the state. According to the Education Commission of the States

(2011), more than 71% of Mississippi’s population did not have an education level equal

to an Associate’s Degree. Student academic success can increase by reducing barriers to

persistence and increasing student retention levels. Improving undergraduate attendance

and enabling student persistence can positively impact a state’s education level leading to

increased economic prosperity among its citizens (Adelman, 1999).

In Mississippi, consistent with trends across the nation, the system of funding

state-sponsored colleges and universities has changed (SB 2851, 2013). In 2011, the

Mississippi Legislature passed an Institutions of Higher Learning Appropriations Bill (SB

2851, 2013), to change the funding of universities to a performance-based allocation

model (Institutions of Higher Learning Board, State of Mississippi, 2013). The new

funding model links state-sponsored institutional funding to completed course credit

hours, academic success, retention rates, and graduation rates (Institutions of Higher

Learning Board, State of Mississippi, 2013). This new funding formula enacted a major

change from earlier funding models, which focused on the total number of enrolled

students (Institutions of Higher Learning Board, State of Mississippi, 2013). The

revamped funding formula changed the focus for colleges and universities from attracting

new students to promoting increased student academic success.

For the university, the present study seeks to determine the influence of an

electronic attendance monitoring system on undergraduate student success. By affecting

10

undergraduate academic success through student retention, the university can expect to

maintain or increase funding levels from the state (Institutions of Higher Learning Board,

State of Mississippi, 2013).

From an individual perspective, on average those who have attained a bachelor’s

degree have a lower unemployment rate, earn $300 more each week than those who have

not earned a bachelor’s degree and develop workplace skills during their collegiate career

(Altonji et al., 2012; Handel, 2003; National Center for Educational Statistics, 2005;

Swanson & Holton, 2009). By analyzing data examining relationships between

attendance, academic success, and literature-based demographics, the present study

focuses on improving the collegiate atmosphere for individuals. Specifically, the present

study fills a gap in the research because previous research has only focused on identifying

one or two demographic traits at a time instead of looking at multiple traits

simultaneously. By looking at multiple demographic factors at the same time, the

opportunity exists to examine relationships between these demographics. Further, the

university chosen for the present study has not previously conducted a similar study.

Therefore, the university and similar institutions could use the findings to develop tactics

to impact students’ academic success based upon the outcomes of the data, or they could

seek to duplicate the study at their site.

Definition of Terms

The present study contains the following terms aiding the reader throughout the

document.

1. Academic Success – When a student achieves a grade of A, B, or C in a course

taught at the university (The University of Southern Mississippi, 2014)

11

2. Participating Classes – The courses where faculty volunteered their class

section(s) for the electronic attendance monitoring system (M. Arrington,

personal communication, 2015)

3. Non-participating Classes – Comparison class sections of the courses taking

part in the electronic attendance monitoring system (M. Arrington, personal

communication, 2015)

4. Electronic Attendance Monitoring System – describes the actual attendance

monitoring procedure, where a member of the university’s Institutional

Research Office was present to collect attendance data (Dicle & Levendis,

2013)

Assumptions of the Study

The four assumptions of the present study concentrate on the classroom

experience between the participating classes that used the electronic attendance

monitoring system and the non-participating classes. First, the researcher assumes all

course sections are equally challenging, regardless of section or course material. The

present study compares course sections that implemented an electronic attendance

monitoring system to course sections that did not implement a monitoring system. If the

faculty teaching the courses are not utilizing similar curriculums, an effect on the

outcome would occur (Shadish, Cook, & Campbell, 2002). Second, the researcher

assumes faculty members use the same grading criteria, including the grading scale and

rigor of grading. The grading scale for the course sections should be similar; as having

one faculty member use one grading scale and another use a different grading scale

would impact the end-of-term course grades (Gump, 2006). Third, the researcher

12

assumes similarity of the physical classrooms for the students enrolled during the

semester of the electronic attendance monitoring system. While the physical

characteristics of classroom selection are out of the control of the researcher, different

dynamics exist in a smaller classroom as opposed to a larger classroom regarding

attendance (Gump, 2006). Last, the researcher assumes the quality of effort and teaching

remains the same regardless of the faculty member tasked with course instruction.

Faculty enthusiasm for teaching a course subject has been shown to impact students’

choice of attendance (Longhurst, 1999).

Delimitations of the Study

Delimitations describe the selections made by the researcher, which set the

constraints for the current research study (Roberts, 2010). The present study used

archived data; therefore, those who implemented the actual program produced the

delimitations. The population of students enrolled during the Spring 2015 Semester

delimits those available for the sample. The Office of Institutional Research selected the

courses in which to offer the opportunity to participate in the electronic attendance

monitoring system (M. Arrington, personal communication, 2015). The system was

implemented only in sections in which the faculty volunteered to participate (M.

Arrington, personal communication, 2015). The volunteering of courses in this fashion

delimits those available to be included in the sample.

Chapter Summary

The present study focuses on the premise that when student academic success is

limited, students face barriers to achieving their degrees (Pascarella & Terenzini, 1991;

Singell & Wadell, 2010). Undergraduate student attendance in collegiate classes,

13

specifically in the general education category, remains problematically low at institutions

across the country (Moore, 2003; Noel et al., 1985; Singell & Wadell, 2010). University

administrators continue to increase focus on academic success.

The present study seeks to determine the influence of an electronic attendance

monitoring system on undergraduate student success in classes that historically have a

low academic success rate. Additionally, the researcher examines multiple attendance-

based demographics to examine relationships between these demographics, attendance

rates, and academic success. Chapter II presents a literature review that focuses on

providing a link between human capital development and student development. In

Chapter III, this present study focuses on the research objectives, research design, data

collection, and analysis. Chapter III discusses the research methods employed, using a

quasi-experimental design using archival data. Analysis of these courses determines if a

difference occurs in the Spring 2015 Semester in the sections of courses using the

electronic attendance monitoring system and the sections of courses that did not use the

system.

14

CHAPTER II – LITERATURE REVIEW

Literature reviews give the opportunity for the researcher to examine earlier

research to support the present study (Roberts, 2010). The purpose of the present study is

to determine the influence of an electronic attendance monitoring system on

undergraduate student success. Chapter II includes a review of relevant literature

regarding the areas of human capital development, student development, undergraduate

student attendance and subsequent behavior changes, how these changes affect academic

success, and undergraduate student demographic factors that affect attendance, and their

connection to the electronic attendance monitoring system. Throughout the development

of the literature review, the researcher conducted multiple searches to find pertinent

material to support the present research study. Multiple literature searches are necessary

so that the researcher can ensure that a robust network of sources helps shape the

conceptual framework and research objectives (Roberts, 2010). The researcher used

various web-based research sites such as Google Scholar and the University of Southern

Mississippi online research library, as well as consulting with reference librarians at the

University.

Defining Human Capital Development

Human capital development theory involves any program or intervention a person

receives to enhance their skills and productivity, which, in turn, creates a positive impact

both for the affiliated organization and for the individual (Smith, 1988). For example, an

organization may have a program that encourages employees to achieve a higher level of

education, including a bachelor’s degree. By promoting an increased education

attainment for employees, the organization allows the individual to interact with subject

15

matter experts in a setting that promotes innovation and skill development (Astin, 1975).

The Bureau of Labor (2015) estimates that individuals who attain a bachelor’s degree

gross approximately one million dollars more in lifetime earnings than people who do not

attain that degree. By pursuing additional education levels, individuals increase their

academic and workplace skills development and affect their personal economic stability

(Noel et al., 1985; Pascarella & Terenzini, 2005). From the employer perspective, having

educated employees in the workplace helps to ensure the company stays competitive

locally, regionally, and globally. Focusing on human capital development within higher

education and increasing opportunities for training and career development can create

employees who will position the organization for global competition (Swanson & Holton,

2009).

Not only does human capital development link closely with student development

and academic success, student development is human capital development. In high-

performing states, partnerships exist between the business sector and academic

institutions (Praxis Strategy Group, 2010a). By linking business and academic partners,

people can collaborate, fostering the opportunity for innovation and feedback to occur in

terms of preparing citizens for industry needs (Praxis Strategy Group, 2010b). Governors

and other government leaders search for ways to offer a competitive advantage for their

state’s citizens by providing workforce development and workplace skills training (Praxis

Strategy Group, 2010b). In addition to providing a competitive advantage for the state or

region, government officials know that human capital development remains one of the

best ways to increase the wealth and to provide economic stability to individuals and

takes a partnership that includes all stakeholders (Florida, 2004; Schultz, 1961). For

16

instance, California developed a higher education master plan in the 1960s to promote

human capital development involving all education partners from the community college

system to the elite universities (Praxis Strategy Group, 2010a). Apparently, the master

plan was successful, as California has one of the more educated populations in the

country (Praxis Strategy Group, 2010b).

When examining human capital development, three domains (psychological,

economic, and systems) link human resource development with undergraduate student

attendance, academic success, and student development (Swanson & Holton, 2009).

These three domains also involve ethics and the relationship that exists with external and

internal environments (Swanson & Holton, 2009). The psychological domain focuses on

human behaviors that influence an organization (Swanson & Holton, 2009). The

economic domain of human resource development relates to the sustainability of

organizations by way of encouraging workplace skill development and succession

planning (Rothwell, 2005; Swanson & Holton, 2009). The systems domain involves the

relationships of organizations and how they assist or hinder the goals of their partnerships

(Swanson & Holton, 2009). These three domains interact, giving a holistic human

resource development approach for professionals in the industry (Swanson & Holton,

2009).

Psychological Domain

The psychological domain helps to explain the relationship between human

capital development and student development. The psychological domain combines

three areas of psychological study: Gestalt psychology, behavioral psychology, and

cognitive psychology (Lee, 2007; Swanson, 1999). Gestalt, which in the German

17

language translates to organization and configuration, explains that employees provide

contributions to influence the experience in a workplace setting in a holistic way (Lee,

2007; Olson & Hergenhahn, 2013; Swanson, 1999). Behavioral psychology speaks to the

idea that people respond to different stimuli based upon their present capacity,

experiences, and the various forces influencing them (Swanson & Holton, 2009).

Cognitive psychology attempts to integrate the Gestalt and behavioral psychology subsets

(Lee, 2007; Swanson, 1999). Cognitive psychology states that humans organize their

lives by goals and around purposes (Tolman, 1948). The psychological domain of human

capital development operates effectively when these three areas interact. Human capital

development professionals may then work to clarify any goals of the organization and

develop the knowledge and ability of individuals, owners, and leaders while considering

the goals and behaviors of all participants (Lee, 2007; Swanson, 1999). The

psychological aspect relates to the present research because, when initiating any new

intervention, especially one that may affect the future, ensuring that everyone

understands the human capital intervention is essential to the project’s success (Fuller,

1997; Kolb & Kolb, 2005; Kotter, 2008; Russell, 2007). Further, the psychological

domain supports undergraduate student attendance by stating that a holistic experience

provides the best path for academic success and workplace skill development. Without

the interactions created by being present in a classroom, a student misses the opportunity

for development.

Economic Domain

The economic domain of human capital development relates to both organizations

and the population. Becker (1993) emphasizes that everyone should view investments in

18

increasing human potential and productivity the same way as investments in traditional

capital improvements. One such supporting theory for the economic domain, the scarce

resource theory, states that everything has an economic significance to an organization or

individual (Swanson, 1999). Scarce resource theory further explains that limitations exist

for everything, which requires us to make hard choices as to how monetary expenditures

provide the greatest return on investment. Further, when considering the scarce resource

theory, organizations focus on the benefit that any investment has on the bottom line for

the organization (Swanson & Gradous, 1986). For example, the environment limits raw

materials; money limits organizations; and time limits humans. The scarce resource

theory states that organizations need to be strategic in the use of capital to receive the

highest return on investment and best impact on the organization (Swanson, 1999).

Scarce resource theory also forces decision makers to prioritize initiatives, ensuring that

each initiative has the appropriate impact they seek (Swanson & Holton, 2009).

The sustainable resource theory and scarce resource theory are similar, but have

one major difference (Swanson & Gradous, 1986; Thurow, 1993). The sustainable

resources theory focuses on long-term implementation of initiatives as opposed to the

scarce resource theory, which focuses on short-term initiatives (Swanson & Gradous,

1986; Thurow, 1993). Sustainable resource theory focuses on using new processes and

technologies to implement initiatives to be successful over extended periods of time

(Swanson & Gradous, 1986; Thurow, 1993). For instance, finding talented and well-

educated employees is essential for the long-term sustainability of organizations

(Thurow, 1993). By promoting the further education of employees, organizations

promote the holistic development of employee’s skills through the interactions between

19

subject matter experts and employees, thus giving the organization a competitive

advantage (Florida, 2004). The employee’s skill based competitive advantage replaces

the global competitive advantage that, in the past, centered on natural resources or capital

funding (Swanson, 1999).

Systems Domain

The third foundation of human capital development lies with incorporating a

holistic systematic approach to promote workplace skill development (Swanson, 1999).

Systems theory allows for the implementation of an intervention designed to increase the

skill development of individuals (Chalofsky, 1992; Swanson, 1999). For individuals

seeking to further their education in an area, systems theory supports the development of

opportunities for learning activities and idea sharing to focus on personal growth to build

skills in individuals and aid in career development (Gilley & Eggland, 1989). Systems

theory dictates the gathering and analysis of performance data to encourage a long-term

focus on skill development and performance improvement (Gilley & Eggland, 1989;

Swanson, 1999; Watkins, 2010). Essentially, systems theory states that people who want

to learn more should do so and their employer or school should encourage that type of

behavior.

Providing the opportunity for students to interact with faculty in the collegiate

environment, while learning workplace skills, is a fundamental core function of the

collegiate atmosphere. By involving the three foundation areas of human resource

development, individuals can build an unbiased opinion on ways to improve the current

climate in an organization or, in this case, the academic success rates in the college

20

classroom. Further, the literature shows the need to change behaviors associated with

low classroom attendance which, by nature, increases the interactions with faculty.

Connecting Human Capital Development and Today’s Higher Education

The exploration of human capital development has existed since the ancient

Greeks began the first education process, which focused on interactions between teachers

(faculty) and students to develop skills. As societies evolved and flourished, other

cultures developed similar systems focused on educating their populace. Over time, these

systems included monasteries, merchants, craft guilds, and apprenticeships leading to the

development of public schools for training the population in basic skills. The interactions

occurred because of the educational opportunities allowed individuals to develop skills.

This is the basis of the education process.

Since the beginning of its existence, higher education sought to create an

opportunity for students to explore intellectual curiosity through enrollment in a variety

of courses, specifically, courses in the liberal arts discipline (Berrett, 2015; Sellingo,

2015). Conversely, today’s culture sets expectations of job attainment for college

graduates (Sellingo, 2015). On February 28, 1967, Ronald Reagan delivered a speech

while serving as Governor of California, during a budget crisis in the state. This speech

was centered on the need for higher education to shift away from intellectual discovery

and toward educating citizens about skills needed for jobs (Berrett, 2015). From this

point on, society began to reshape views of college, and cultural changes began to

reinforce the need for more practical degrees, as opposed to exploration of the liberal arts

(Berrett, 2015). With the change in educational philosophy, students became customers,

and the higher education community transformed into a venue where students learn

21

workplace skills needed to become productive members of society (Berrett, 2015).

Regardless of the change, the interactions between faculty and students continued to be

important, to ensure the environment existed to promote learning and innovation. As the

change in educational philosophy continued, so did the mechanisms for publicly funding

state colleges and universities.

Public higher education institutions derive funding through a variety of financial

sources, including tuition dollars, private giving, and state allocations. In 1978, Pfeffer

and Salancik introduced the notion of resource dependency theory, which says that

organizations depend on their internal and external environments for resources. Internal

environments serve those within the university, while external environments, such as

budgets and world affairs affect the university. Further, Pfeffer and Salancik (1978)

explain that the organization’s behavior responds to changes in environments. If a

resource change occurs, then the entity must adapt to the changes, preventing the change

from destabilizing the organization. To guarantee the survival of the institution, this

adaptation to change must happen (Pfeffer & Salancik, 1978). For colleges and

universities in Mississippi, the need to adapt to a change in external resources is

paramount. In 2013, The Mississippi Institutions of Higher Learning Board—the

external entity that directs funding—changed to a new funding model for colleges and

universities which specifically distributes 85% of an institution’s state funding based on

students’ academic success and graduation statistics (Institutions of Higher Learning

Board, State of Mississippi, 2013). As such, colleges and universities in Mississippi must

adapt to this change in their funding resource to prevent destabilization of their

organizations.

22

Astin (1975) stated that a focus on academic success, by encouraging faculty and

student interaction, promotes higher retention and graduation rates. At The University of

Southern Mississippi, entering freshman students complete an average of 85% of

attempted courses, but the first-year retention rate is 75% for the university (Institutions

of Higher Learning Board, State of Mississippi, 2014). In 2014, The University of

Southern Mississippi’s Student Success Committee issued a report focused on improving

academic success rates. One suggested method of doing so focuses on the

implementation of an electronic attendance monitoring system (The University of

Southern Mississippi, 2014).

Student Development and Human Capital Development

Promoting student development through interactions between faculty and students

increases academic success during the collegiate years and sets up a critical base for the

future success of individuals (Astin, 1993). While examples exist of individuals who

enjoy success without a bachelor’s degree, many people need the academic and

workplace skill development that occurs while achieving a degree to experience job and

income security (De Gregorio & Lee, 2002). In 2009, more than 75% of available jobs

needed an education level above a high school degree (White House, Office of the Press

Secretary, 2009). Per the Bureau of Labor Statistics (Torpey & Watson, 2014), the

number of available jobs needing at least a high school education has held steady at 73%.

Becker (1993) introduced research correlating higher salaries with more highly educated

employees. These employees develop enhanced workplace skills and higher productivity

rates through academic interactions between faculty and students (Becker, 1993).

Further, the more highly educated an individual becomes, the greater the benefit to the

23

community (Florida, 2004). Focusing on academic success strategies offers benefits for

all involved at the university and community level (Astin, 1975).

Social Interaction

Research over the past four decades link student involvement to higher retention

and academic success (American College Testing, 2010; Bean & Metzeler, 1985; Berger

& Braxton, 1998; Pascarella & Terenzini, 2005; Tinto, 1993). Tinto (1993) reports that

sustained social interaction among faculty and students improve knowledge retention.

Further, the higher the level of student involvement or engagement, the more positive

impact a student receives in the areas of academic and workplace skill development

(Tinto, 1993). When students pursue post-high school education, they continue to focus

on academic and workplace skill development, refining learning processes through social

interaction following Kolb’s theory of experiential learning (Kolb & Kolb, 2005). In

other words, to develop intellectually and socially, students need to interact with others

and with the faculty who are the subject matter experts (Kolb & Kolb, 2005). Students

arrive at college with expectations and perceptions of a campus culture, and universities

strive to aid students as they assimilate to this culture (Gerdes & Mallinckrodt, 1994;

Harrison, 2006; Lowis & Castley, 2008). Astin (1975) surmises that the more

interactions between faculty and students in the classroom during their collegiate career,

the higher the likelihood of retention. Depending upon the campus of enrollment, a

student could choose from several student organizations—such as general and specialized

organizations, professional organizations, honors organizations, or Greek Life

organizations—to gain both personal development and social interaction. As a strategy,

universities should promote social interaction of undergraduate students and faculty to

24

increase student retention and graduation rates (Astin, 1975; Noel et al., 1985). When

involved in campus organizations, students develop workplace skills through both the

social involvement between their peers and their interaction with university faculty and

staff (Patton, Renn, Guido, & Quaye, 2016).

Workplace Skill Development

The development of workplace skills—such as autonomy, personal integration,

impulse expression, introverted thinking, and complex thinking—occurs heavily in the

college years (Patton et al., 2016; Chickering & McCormack, 1973; Gianoutsos, 2011;

Sternberg, 2013). According to Gianoutsos (2011) and Sternberg (2013), employers

report difficulty finding the previously mentioned workplace skills because many

students leave higher education institutions before completing a degree plan. The

negative impact on human capital development affects the local area due to the lack of

refined workplace skills one receives in the postsecondary education setting (Patton et al.,

2016). Companies continue to spend more time training employees on skills not

developed as part of the collegiate experience (Cappelli, 2011). Thus, academic success

remains a priority for everyone at a college or university as students are trained to meet

the needs of the business world.

Connecting Attendance, Student Development, Academic Success and Human Capital

Development

Higher education offers an avenue for the development of skills in a focused

subject area. Providing opportunities for subject specific knowledge through the higher

education process supports human capital development, defined as follows:

25

Any process or activity that, either initially or over the long term, has the potential

to develop adults’ work-based knowledge, expertise, productivity, and

satisfaction, whether for personal, group, or team gain, or for the benefit of an

organization, community, nation, or ultimately, the whole of humanity. (McLean

& McLean, 2001, p. 315)

Offering opportunities for students to have a peer-to-peer interaction, as well as a faculty-

student interaction, are principal factors supporting academic success (Noel et al., 1985).

Creating an environment centered on academic success and workplace skill

building leads to increases in student attendance, involvement, and academic success

(Bailey & Morais, 2005). The Student Success Steering Committee had the following to

say about the importance of undergraduate student attendance:

Studies show that class attendance can significantly improve academic

performance and lower the time it takes to complete a degree. Regular class

attendance promotes faculty/student engagement and allows for early assessment

of the student’s strengths and weaknesses. Absences more than 10% of the

scheduled classes are detrimental to a student’s chance of success. (The

University of Southern Mississippi, 2014, p. 10)

Just as human capital development professionals find themselves in an ever-

changing and innovative society, so do the professionals in higher education. Because

constant change occurs, post-secondary education must adapt, giving university

professionals the opportunity to interact frequently with students who are the future

workforce. These interactions are critical to the development of human capital, as the

26

three domains of human capital development show; thus, they are important to the local

community, the state, the region, and society as a whole (Becker, 1993; Florida, 2004).

Human Capital Development and Mississippi

Human resource is, simply put, the development of human capital or people

(Swanson & Holton, 2009). Human capital includes the skills, knowledge, competencies,

social network, professional network, creativity, and personal attributes humans have

(McLean & McLean, 2001). People find employment in a multitude of organizations,

and each type of organization and job requires a different skill set. Therefore, leaders

must create plans to support the continuous education of their employees by

incorporating research, theory, and practice into the development of programs (Marsick

& Watkins, 1994). Then, as organizations lay a path to carry out their goals, it is

paramount to remember how human capital development aims to create productive and

educated citizens of society (Swanson & Holton, 2009). The collegiate setting offers one

place for this education to occur by increasing the academic and workplace skills of the

future workforce. Because of this, academic success and human capital development

intertwine. Becker (1993), a pioneer in the human capital research field, stressed that the

human capital sector relies on skilled workers. President Obama argued that education

provides the best means to create these skilled workers (White House, Office of the Press

Secretary, 2009). In his joint address to Congress in 2009, President Obama said the

following:

Right now, three-quarters of the fastest-growing occupations require more than a

high school education. And yet, just over half of our citizens have that level of

education. We have one of the highest high school dropout rates of any

27

industrialized nation and half of the students who begin college never finish. This

is a prescription for economic decline, because we know the countries that out-

teach us today will out-compete us tomorrow. That is why it will be the goal of

this administration to ensure that every child has access to a complete and

competitive education from the day they are born to the day they begin a career.

(para. 62)

In his speech, President Obama stated 75% of the available jobs now require above a high

school education, adding that the country must establish a pathway to success through

educational opportunities (White House, Office of the Press Secretary, 2009). Businesses

consider education level of the local population before investing in a geographic area

(Florida, 2004). Education increases the knowledge level of potential employees and

offers one of the most important capital investments that an organization or region can

make (Florida, 2004). To that effect, the higher the education a person achieves, the

more mobile the person becomes, which leads to greater opportunities, income security,

and wages for the individual (Schultz, 1961). The Bureau of Labor Statistics (2015)

recently published a table describing the differences in earning and unemployment rates

based upon education level. Figure 2 reinforces the President’s remarks made before

Congress and provides a parallel to the state of education in Mississippi. As Chapter II

continues, research, figures, and tables highlight human capital development and its

importance to Mississippi.

28

Figure 2. Educational attainment of working adults aged 25 to 64

Note. Net Migration of 22 to 64 year olds by education level (2005-2009). Adapted from “College Completion in Mississippi: The Impact on the Workforce and the Economy” by Education Commission of the States, 2011. Copyright 2011 by Education Commission

of the States. Adapted with permission of the publisher. See Appendix A for statement of permission from organization.

In 2013, the Mississippi Legislature passed an Institutions of Higher Learning

Appropriations Bill (SB 2851, 2013), adopting a performance-based allocation model

linking institutional funding to completed course credit hours, retention rates, and

graduation rates (Institutions of Higher Learning Board, State of Mississippi, 2013). The

new funding formula—the performance-based allocation model—caused a meaningful

change from earlier funding models, which focused primarily on the total number of

enrolled students at each university. The new design created a shift in focus from student

enrollment to student retention by distributing state educational funds based upon

retention and graduation rates, not overall numbers (Institutions of Higher Learning

Board, State of Mississippi, 2013).

16.5

30.1

24.5

8.1

13.6

7.2

12.6

27

22.2

8.4

19.1

10.78.8

23.8

17.2

8.4

24.3

17.4

0

5

10

15

20

25

30

35

Less than HighSchool

High School Some College,No Degree

Associate Degree Bachelor'sDegree

Graduate,Professional

Degree

Mississippi US Average Massachusetts (Most Educated)

29

In the past, state-funded public institutions of higher education in Mississippi

have relied on two major sources of funding: state appropriations and student tuition.

Between fiscal years 2008 and 2014, state appropriations for higher education decreased

by 25% across the nation (Mitchell, Palacios, & Leachman, 2014). The adjustment in

funding has affected many state higher education budgets. A new tactic sees higher

education institutions’ funding tied to performance metrics (Quinton, 2016). In fact, 26

states have already adopted the tactic with another 10 states considering adopting the new

way of distributing state funding for institutions of higher education (Quinton, 2016).

With changes and innovation coming to higher education funding, citizens and

lawmakers must make sure future students have access to education. In addition to the

state placing an increased focus on academic success, the Southern Association of

Colleges and Schools asked universities to develop a five-year strategic initiative focused

on the success of current students (Southern Association of Colleges and Schools

Commission on Colleges, 2015). The University of Southern Mississippi has chosen to

develop their Quality Enhancement Plan to obstacles to academic success, and by

extension, student retention.

The Organization of Economic Co-operation and Development (2014) ranks the

United States 14th out of 37 countries as it pertains to college degree completion.

According to Figure 2, in 2011, only 29.8% of adults in the United States aged 25-34 had

a bachelor’s degree or higher (Education Commission of the States, 2011). Regarding

the present study, the eight four-year publicly financed universities in Mississippi have a

joint six-year graduation rate average of 50% among students who make the decision to

enroll in a bachelor’s degree program (Institutions of Higher Learning Board, State of

30

Mississippi, 2014). To address this graduation rate issue, the Institutions of Higher

Learning reconfigured the funding methods to reward successful schools by focusing on

academic success, retention, and graduation (Institutions of Higher Learning Board, State

of Mississippi, 2013). Schiller (2008) states,

Research shows that having large numbers of college graduates in a region

increases that region’s economic growth and that spillovers (also called

externalities) are an important factor in generating more rapid growth. Aware of

this connection, educators, state and local governments, and businesses around the

country are making efforts to increase the educational attainment of their local

workforces, especially the number of college graduates. (para. 16)

According to Schiller (2008), a clear link exists between human capital development and

college academic success, and by proxy, student retention, which over time leads to

graduation.

Human Capital Development Today

Employers operate in an environment that expects quick change, adaptability, and

results (Miller & Ireland, 2005). As students prepare for the workplace, they must

develop the proper academic and workplace skills needed to prosper; these workplace

skills develop while a student attends college. Chickering and McCormack (1973) define

some of these skills as personal integration, complexity, impulse expression, autonomy,

estheticism, and introverted thinking. Further, research from Chickering (1974) and

Handel (2003) offers that confidence, integration to social scenes, increased job-specific

skills, awareness of situations, and stability are examples of workplace skills developed

by students as they progress through college. However, many researchers, policy makers,

31

and organizations believe employees’ work-related skills do not match the requirements

for existing jobs (Handel, 2003).

Figure 3. Median annual wages for employed workers aged 25 to 64 by level of

education

Note. Net Migration of 22 to 64 year olds by education level (2005-2009). Adapted from “College Completion in Mississippi: The

Impact on the Workforce and the Economy” by Education Commission of the States, 2011. Copyright 2011 by Education Commission

of the States. Adapted with permission of the publisher. See Appendix A for statement of permission from organization.

Companies and states’ prosperity rely on preparing a viable workforce and

developing a sustainable pool of human capital talent (Florida, 2004). Figure 3 presents

information on the status of the median annual wages for employed workers aged 25 to

64 by their level of education as originally provided by the Education Commission of the

States (2011). The information, gathered in 2011, would have been available to inform

decisions to adjust the funding formula for the Institutions of Higher Learning in

Mississippi. As states look to offer opportunities for their citizens, the focus on education

17,991

24,487

28,985

34,382

39,979

49,974

29,984

19,990

27,985

31,983

37,980

49,974

64,966

35,681

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

Less ThanHigh School

High SchoolGraduate or

GED

Some College,No Degree

AssociateDegree

Bachelor'sDegree

Graduate orProfessional

Degree

All Workers

Mississippi US Average

32

attainment remains at the forefront of initiatives for the country and for the individual

states to improve the financial security of residents.

Human Performance Interventions and Behavior Change

Interventions are tactics used in human capital development to promote change

through a coordinated improvement process (Fuller, 1997). Specifically, interventions in

human capital development revolve around theories of action designed to increase

effectiveness (Argyris & Schon, 1982; Fuller, 1997). Interventions can take many forms,

including job aids, training games, rewards, recognition, team building, conflict

management, process redesign, and newsletters (Fuller, 1997).

Before beginning any intervention, leaders need to obtain information on the

employees or group members taking part in the intervention process (Argyris & Schon,

1982; Russell, 2007). When implementing a change in an organization, leaders provide

the knowledge and ideas needed for the betterment of the organization (Kotter, 2008).

Testing the impact of a change could occur by creating employee focus groups, testing on

a small population of employees while measuring the outcomes, or examining similar

programs at other organizations (Kotter, 2008). Researchers test the program’s

effectiveness by establishing a control group to compare the data from the beginning of a

program to after the program concludes (Argyris & Schon, 1982; Fuller, 1997).

However, the programs sometimes rely on the premise that people enact change when

they realize their behavior has the opportunity for improvement (Argyris & Schon, 1982;

Fuller, 1997; Kotter, 2008). The researcher of this present study seeks to find if the

implementation of an electronic attendance monitoring system affects academic success

33

rates (Dicle & Levendis, 2013; O’Connor, 2010; University of Southern Mississippi,

2014).

Human Performance Interventions

Colleges and universities adopt different innovative strategies to improve

academic success rates. In college, all students, especially undergraduates, make

decisions affecting their behaviors, including class attendance (Newman-Ford,

Fitzgibbon, Lloyd, & Thomas, 2008). Some reasons for student absenteeism are travel

and weather, external commitments (work, volunteering, sports), illness, peer group

influence, parental influence, and college discipline (Longhurst, 1999). Additionally,

student perception of course rigor and attitudes regarding the importance of attendance

can also affect attendance rates (Gump, 2006).

From a university perspective, one of the most effective strategies to improve

academic success rates is to focus on undergraduate student attendance and promote

interaction between faculty and students (Romer, 1993). Just as in a job setting, people

are not able to perform at their best if they are not in attendance. The same holds true for

the collegiate classroom: A person’s ability to absorb information increases with his or

her presence in the classroom. Crede et al. (2010) and Moore (2003) found that increased

attendance rates of undergraduate students positively affected academic success and that

a student’s attendance in a classroom setting provides a stronger indicator of success than

high school grade point averages or standardized test scores. Bligh (1998) found that

undergraduate students who regularly attend lectures achieve higher grades due to their

attendance and interaction with faculty and students. Research shows that the time

students spend attending class positively affects their end of course academic grade

34

(Stanca, 2004; Thomas & Higbee, 2000; Vidler, 1980). Further, increasing

undergraduate student classroom attendance and interactions with faculty can improve

academic and workplace skill development, while also increasing students’ motivation

levels to succeed (Kanfer & Ackerman, 1989).

Attendance Monitoring Systems

To find the potential effect of undergraduate student attendance, universities such

as The University of Mississippi, University of Glamorgan (United Kingdom), Loyola

University New Orleans, and the University of Northern Arizona have each recently

implemented attendance-monitoring software systems on their campuses (Dicle &

Levendis, 2013; Newman-Ford et al., 2008; Newsom, 2016; O’Connor, 2010). By

encouraging undergraduate student attendance in the classroom, universities like the ones

mentioned above have the potential to aid students in academic growth by promoting

interaction.

Loyola University New Orleans’ attendance-monitoring software, developed by

two faculty in the College of Business, uses radio frequency identification chips,

embedded in the student ID card to log a student’s attendance (Dicle & Levendis, 2013).

Of the students using the technology, 97.5% approved of the method to track

undergraduate student attendance, while the faculty appreciated not needing to determine

attendance for each class meeting (Dicle & Levendis, 2013). The University of Northern

Arizona funded their program through the federal stimulus to support education

initiatives (O'Connor, 2010). The project outfitted one building under construction with

the new attendance trackers. The identification card system used by the students already

had the radio frequency identification chips embedded. The RFID type of intervention

35

took years of planning to implement (O'Connor, 2010). Within Mississippi, the

University of Mississippi (Ole Miss) has adopted a similar strategy of attendance

monitoring by using wall mounted card scanners (Wiley, 2013). Ole Miss uses a system

where students scan the barcode on their ID cards, registering their attendance in a

system that faculty and the university can use to track attendance and provide data for

other university reports. By monitoring attendance, Ole Miss allows for statistical

modeling to occur and places emphasis on the interaction between students and faculty

(Newsom, 2016). The University of Glamorgan (United Kingdom) used a formalized

attendance monitoring system, which allowed for data-sharing with the university

community that showed expected grades of students based upon their absenteeism

(Newman-Ford et al., 2008). Electronic attendance monitoring systems allow

universities to find students needing interventions to improve their opportunity to succeed

academically (Newman-Ford et al., 2008).

Implementing Behavior Change Practices

Human capital development practices incorporate improvement processes to

promote changes in behavior (Fuller, 1997). These behavior changes in human capital

development revolve around theories of action designed to increase effectiveness

(Argyris & Schon, 1982; Fuller, 1997). Testing the impact of a behavior change occurs

through many different facets such as creating employee focus groups or conducting a

test on a small population (Kotter, 2008). No matter the format, researchers must always

measure the outcomes of behavior change endeavors (Kotter, 2008). Researchers use

data from a control group to compare the beginning and end points of the intervention

and to establish the behavior change’s effectiveness (Argyris & Schon, 1982; Fuller,

36

1997). However, the behavior change endeavors incorporate that individuals change

upon realizing that improvements can be made to their behaviors (Argyris & Schon,

1982; Fuller, 1997; Kotter, 2008). The researcher of this present study seeks to find if the

formal attendance-monitoring procedure affects academic success rates (Dicle &

Levendis, 2013; O’Connor, 2010; University of Southern Mississippi, 2014).

When looking to implement behavior change practices, two theories support such

endeavors, The transtheoretical model of change and the theory of planned behavior. The

psychological domain for human capital development serves as a base for observing and

as a starting point to research behavioral changes (Epstein & Sheldon, 2002; Tanner-

Smith & Wilson, 2013). School attendance provides not only the opportunity for

interactions between faculty and students, but also the opportunity to develop social and

workplace related skills. (Epstein & Sheldon, 2002; Tanner-Smith & Wilson, 2013)

The transtheoretical model of change is a multistep process that sees individuals

work through intentional behavior changes (Edwards-Stewart, Prochaska, Smolenski,

Saul, & Reger, 2017). The theory applies to a variety of settings, populations, and

behaviors presenting different stages that individuals progress through (Prochaska et al.,

2008). The different stages of the transtheoretical model of change are pre-

contemplation, contemplation, preparation, action, and maintenance (Edwards-Stewart et

al., 2017). Individuals move through these stages at different paces when attempting to

modify a behavior. Many people envision behavior change as a single event that occurs

to correct a behavior (Epstein & Sheldon, 2002; Tanner-Smith & Wilson, 2013).

However, the transtheoretical model of change suggests that time plays an important part

in the behavior change process.

37

Transtheoretical Model of Change

As individuals prepare for behavior changes, they must realize their starting

points in the process. The different stages of the transtheoretical model of change

provide a framework for individuals to assess their readiness to implement change. In

order to implement the changes, individuals must work through these stages in order.

The first stage, pre-contemplation, takes place when individuals are not ready to act on a

needed behavior change in the next six months. Sometimes, individuals become stuck in

this stage because they lack the information necessary about the consequences of their

existing behavior. When multiple unsuccessful attempts occur, individuals can become

resistant to implementing the needed change. The second stage, contemplation, occurs

when an individual intends to make a change in the next six months. Individuals in this

stage are aware of the benefits if they implement the behavior change but are not willing

to take immediate action to implement the change. The preparation stage occurs as

individuals realize the need to take action immediately to affect their lives. Individuals in

this stage have already taken some steps to address their shortcomings or distractions and

stand ready to implement behavior change actions. The action stage occurs as individuals

make specific changes or modifications to behaviors in their lives. These actions are

observable by others, who see an individual taking specific actions to correct a behavior

shortcoming. The final stage of the transtheoretical model of change is the maintenance

stage. The maintenance stage occurs when individuals have implemented the needed

behavior changes over a period of time and have taken necessary precautions to prevent a

relapse. Individuals in the maintenance stage also become accustomed to the new

behavior and recognize that the behavior changes have resulted in distinct benefits to

38

their life. The transtheoretical model of change lays the foundation for individuals who

are ready to implement behavior changes.

Theory of Planned Behavior

The theory of planned behavior attempts to provide explanations for individual’s

behavior intentions in specific circumstances (Kautonen, Gelderen, & Fink, 2015). The

theory of planned behavior involves an individual’s behavior intent and how outcomes,

risks, and benefits of the intended behavior impact the intentions (Kautonen et al., 2015).

The most integral part of the theory comes from the motivation that an individual has,

giving them the ability to change their behavior. The attitudes that an individual

possesses includes the individual’s perception of the end result of performing the

behavior. In other words, the stronger the motivation a person possesses to perform a

behavior, the more likely they are to follow through. In the classroom setting, the

expected normal behavior is that enrolled students attend the class meetings for each

course. By implementing the electronic attendance monitoring system, the researcher can

rely on the theory of planned behavior and transtheoretical model of change enabling

enrolled students to make a conscious decision as to whether or not their behavior needs

to change. Further, the results of the electronic attendance monitoring system enable the

researcher, through the incorporation of demographic variables, to identify at-risk

individuals enrolled in future courses.

Similar to other universities’ undergraduate student-attendance intervention

programs, The University of Southern Mississippi’s Student Success Initiative report

focuses on increasing attendance by linking to the Freshman Attendance-Based Initiative

(The University of Southern Mississippi, 2014; Wiley, 2013). Because of changes in

39

state funding from the Institutions of Higher Learning Board, The University of Southern

Mississippi, like other institutions in the state, now focuses on new academic success

endeavors. The consequences of not implementing strategic endeavors, such as the

electronic attendance monitoring system, could include a lower level of funding from the

state. Table 1 highlights the student graduation rates from the fall 2005 freshman cohort

that was available the most recent data available prior to implementing the electronic

attendance monitoring system. The descriptive statistics outline the necessity for change

to occur when comparing the success of students to the statewide average.

Table 1

Fall 2005 Freshman Cohort Student Graduation Rates

Note. Student Graduation Rates of Fall 2005 Freshman Cohort. Adapted from “Factbook 2014/2015 Retention” by The University of

Southern Mississippi, 2015. From The University of Southern Mississippi Office of Institutional Research, 2015. Document available

for public consumption.

Demographics, College Success, and Undergraduate Student Attendance

Demographics identify personal traits of participants and these traits also have the

ability to influence academic success and, by proxy, student retention. Discovering traits

that influence academic success provides the data to develop proper strategies to aid in

retention of these students (Tharp, 1998). Following the student’s grade point average,

attendance, which promotes interaction in the classroom between faculty and students,

Student Cohort USM Average Mississippi Average

First-time, full-time,

Freshman Fall 2005 1,328 (100%) 1,009 (100%)

Graduating within 4 years

(100% of normal time) 296 (22.3%) 271 (26.8%)

Graduating within 6 years

(150% of normal time) 623 (46.9%) 502 (49.8%)

Graduating within 8 years

(200% of normal time) 676 (50.9%) 537 (53.2%)

40

has been indicated to be the next highest factor in student academic performance (Sauers,

McVay, & Deppa, 2005). Demographic factors affecting attendance are used in the

present study and in past research. The present study examines the following

undergraduate student demographic factors affecting attendance: gender, local residency,

state residency, Greek Life organization affiliation, admission type, cumulative GPA,

age, undergraduate year classification, ethnicity, current enrollment status, and the

semester of course enrollment. In previous research (Altermatt, 2007; Behar, 2010;

Borland & Howsen, 1998; Caldas, 1993; Chickering, 1974; Lamdin, 1996; Lowis &

Castley, 2008; Newman-Ford et al., 2008; Romer, 1993; Stewart, Merrill, & Saluri, 1985;

Stewart & Rue, 1983), these authors, focused on the importance of undergraduate student

attendance with one of the attendance-based demographics previously mentioned. A

shortcoming of their research lies with focusing on only one demographic factor at a

time. Additionally, in the past, many researchers have observed undergraduate student

attendance rates aggregated on the course or school-wide level but have not integrated

this data with student demographic factors (Borland & Howsen, 1998; Caldas, 1993;

Farsides & Woodfield, 2003; Lamdin, 1996; Romer, 1993; Woodfield, Jessop, &

McMillan, 2006). Combining the demographic options can lead to developing proper

strategies to improve academic success rates and to identify which types of student are

less likely to graduate (Tharp, 1998). By analyzing student demographics, researchers

may be able to identify patterns between the various demographic factors that affect

academic success. The purpose of the present study is to determine the influence of an

electronic attendance monitoring system on undergraduate student success.

41

Gender

Research shows differences in male and female academic success levels in higher

education (Altermatt, 2007; Behar, 2010; Newman-Ford et al., 2008). The National

Center for Educational Statistics (2005) reported that the percentage of women who have

successfully pursued a bachelor’s degree has increased from 61% in the 1970s to 71% in

the 1990s. Conversely, the bachelor’s degree attainment for men has stagnated at 61%

since the 1970s (National Center for Educational Statistics, 2005). The gender divide

continues to grow, with women outnumbering men attending post-secondary institutions

(U.S. Census Bureau, 2006; U.S. Department of Education, 2016). According to the U.S.

Census Bureau (2006), more women under the age of 45 have bachelor’s degrees than

men in the same age range. Further, research shows that gender influences students’

academic success (Altermatt, 2007; Newman-Ford et al., 2008). Arredondo and Knight

(2005) and Newman-Ford et al. (2008) found that gender played a role in students’

decision to attend class. Research by Woodfield et al., (2006) found that men, especially

men with higher GPAs, were more likely to be absent from class. In contrast, women

were more likely to be present for class meetings (Woodfield et al., 2006). Previous

research supports gender playing a role in academic success and attendance in the

classroom (Adelman, 1999; Altermatt, 2007; Arredondo & Knight, 2005; Astin &

Oseguera, 2005; Behar, 2010; Campbell & Fugua, 2008; Crisp, Horn, Dizinmo, &

Barlow, 2013, Newman-Ford et al., 2008).

Local Residency

Through more than 20 years of research, Pascarella and Terenzini (1991) found

that on-campus students tend to achieve higher academically. Pascarella and Terenzini

42

(2005) later reaffirmed their research, reporting that the retention of resident students

remains consistently higher than that of commuter students. Research shows that

students who do not live on campus are less committed academically (Chickering, 1974;

Lowis & Castley, 2008; Stewart et al., 1985; Stewart & Rue, 1983). Students who live

on campus make a higher investment in developing relationships that exist both outside

of the classroom and in their residence halls (Lowis & Castley, 2008). Further,

interactions with peers aid both student’s academic success and their social integration

into the campus community (Lakey & Cohen, 2000). By residing on campus, resident

students have the opportunity to participate in more activities outside of the classroom,

promoting interaction with other students and faculty (Pascarella & Terenzini, 2005).

Stewart et al. (1985) and Stewart and Rue (1983) reported that commuting students are

more easily disrupted, not only by not attending classes but also by withdrawing from

school, thus making the withdrawal from college a less intrusive decision. Facilities that

students live in during their college careers can have an impact on their academic success

by motivating them to attend class (Lau, 2003). The on-campus residence facilities have

support staff and other students that provide motivation to attend class and encourage

academic success (Pascarella & Terenzini, 2005). By including local residency as a

demographic factor, the data exposes whether on-campus or off-campus living leads to

higher academic success in the courses participating in the electronic attendance

monitoring system.

State Residency

In addition to local residency, the demographics include whether a student is a

resident of Mississippi or is a non-resident student. Both local and state residency

43

information give valuable information when evaluating academic success (Arredondo &

Knight, 2005). Previous research (Arredondo & Knight, 2005; Chimka et al., 2007)

shows that state residency information relates to undergraduate student’s decision to

attend class. Students coming from out of state have lower academic success rates and

college completion rates than students with in-state residence (Arredondo & Knight,

2005). Mentally, these students feel disconnected from past social support networks due

to the distance of the students’ residences from the institution (Chimka, Reed-Rhoads, &

Barker, 2007). The attitudes of these students could affect their decision to attend class

on any certain date.

Greek Life Organization Affiliation

The organizations that a student becomes involved with can also influence

academic success (Berger & Braxton, 1998). Previous research has shown that students

involved in at least one significant campus activity have higher rates of academic success

and retention (American College Testing, 2010; Pascarella & Terenzini, 2005). Tinto

(1993) states that the support networks formed through organizational membership

positively influence a student’s academic success. Kuykendall (2008) expanded upon

Tinto’s research by adding that students are more likely to succeed in college when they

are involved in campus activities. Further, Astin (1984) stated that when students spend

more time on campus, especially in the classroom, it is likely they interact with a wider

array of campus entities, thus leading to increased academic success. Berger and Milem

(1999) added that an undergraduate student’s campus involvement leads to higher levels

of academic success and that a student’s involvement level can influence their decision to

attend classes, due to their attachment to the university. At The University of Southern

44

Mississippi, all Greek Life organizations houses are on campus; thus, students who

participate in a Greek Life organization shape a positive experience by having on-campus

resident status (Pascarella & Terenzini, 2005).

Research has found that students who are involved with at least one major campus

organization and have an elevated level of motivation are more successful than the

general student body population (American College Testing, 2010). However, in a

review of the literature, previous research has not measured the link between

undergraduate student attendance rates and Greek affiliation. According to the Office of

Institutional Research, the only consistent student involvement is in Greek Life

organizations (M. Arrington, personal communication, 2015). Involving Greek Life

membership as a demographic during the electronic attendance monitoring system could

be strategic in providing additional data.

Admission Type

The admission type of students can also play a role in academic success (Noel et

al., 1985). Examining the admission type of students is of interest in the present study

because, in Mississippi, 15 two-year colleges offer similar campus amenities to four-year

institutions, such as classes, training, residential facilities, and athletic team competitions

(Mississippi Community College Board, 2016). The community college system presents

students with local options to begin their education. Students have the choice to

complete their associate degree or to transfer their desired number of credit hours to a

four-year institution. Further, The University of Southern Mississippi has the highest

number of transfer students of the eight publicly funded universities in Mississippi (M.

Arrington, personal communication, 2015). The University of Southern Mississippi uses

45

the designation of freshman admit and transfer admit for student admittance applications

(M. Arrington, personal communication, 2015). Yu, DiGangi, Jamnasch-Pennell, and

Kaprolet (2010) found that students with transferred hours are more likely to demonstrate

academic success, but there is a lack of research involving admission type and

undergraduate student attendance rates. Thus, identifying whether or not there is a

difference in the academic success rates and attendance rates of freshman admitted or

transfer admitted students would yield usable data.

Cumulative Grade Point Average

Another demographic factor to examine for its influence during the present study

includes a student’s grade point average (GPA). GPA measures academic success at the

end of every term or quarter (Great Schools Partnership, 2014). The GPA metric

demonstrates a student’s progress through school until graduation and provides the

student with a snapshot of his or her academic achievement (Great Schools Partnership,

2014). The cumulative GPA is a summary of a student’s achievement level, with all

classes or courses figured into the metric. The GPA is one recognized metric for success.

Many universities require students to maintain a minimum cumulative GPA to graduate.

Further, the federal government relies on each university to determine a cumulative grade

point average in determining a student’s eligibility for financial aid (The University of

Southern Mississippi, 2016). Singell and Waddell (2010) asserted that by using GPA

information, administrators would be able to recognize potential academic achievement

risks, allowing them to intervene as needed. Singell and Wadell’s (2010) analysis shows

a positive correlation between student retention, GPA, and academic success. Further,

monitoring undergraduate student attendance is indicated to have a positive and

46

significant outcome on final course grades (Caldas, 1993; Farsides & Woodfield, 2003;

Lamdin, 1996; Rau & Durand, 2000; Romer, 1993). One can infer that the higher a

student’s GPA, the higher the number of courses a student has successfully completed.

Age

Age can influence students’ success rates in courses, as non-traditional students

have a lower graduation rate than traditionally aged students (Markle, 2015). The present

study could show that a difference exists between traditionally aged and non-traditionally

aged students when it comes to academic success, as well as a difference in the academic

success rates for traditional-aged and non-traditional-aged students, as noted in Pascarella

and Terenzini’s (1991) research. The quality and quantity of the interactions that occur

between students, faculty, and staff affect students (Pascarella & Terenzini, 1991).

Similarly, Astin (1984) stated that the social interactions students have on campus with

faculty contribute to the development of skills. Tinto (1993, 2006) found that non-

traditional students faced limitations through fewer on-campus interactions when

compared to traditional-aged students. Markle’s (2015) research found that there was no

difference between non-traditional males and females in their academic success and

graduation rates. Crede et al. (2010) found that students who were older attended class

more often than students who are younger and that it factored into their end-of-course

grades.

Undergraduate Year Classification

Undergraduate year classification describes a student using the established terms

of freshman, sophomore, junior, or senior. The undergraduate year classifications are

determined by the university, but the general standard across the country is that freshmen

47

have fewer than 30 successfully completed credit hours; sophomores have between 30

and 60 successfully completed credit hours; juniors have between 60 and 90 successfully

completed credit hours; and seniors have more than 90 successfully completed credit

hours (The University of Southern Mississippi, 2016). A successfully completed credit

hour occurs when the student completes a course with a grade of A, B, or C, and sees

those hours added to their cumulative total number of completed course hours. By

including the undergraduate year classification demographic, the present study analyzes

whether a student’s decision about when to enroll in the participating courses makes a

difference in academic success. In a review of the current literature, studies have not

measured the interaction of different classification levels and attendance rates. In terms

of academic success, Crede et al. (2010) found a significance between classification,

undergraduate student attendance, and academic success. In addition, one can infer that

as students matriculate through college, they are successful because they remain enrolled;

students with unsatisfactory academic success frequently choose not to continue.

Ethnicity

Ethnicity is also an important demographic, as the present study aims to identify

influences that affect academic success in the courses observed (Zea, Reisen, Beil, &

Caplan, 1997). Rodgers (2013) found that in the United Kingdom, minority students

were less likely to complete their degrees when compared to other students. Historically

Black Colleges and Universities saw higher graduation rates among African-American

students as compared to students at Predominately-White Institutions (Allen, Epps, &

Haniff, 1991; Pascarella & Terenzini, 2005). Astin and Oseguera (2005) found that,

overall, Whites and Asians were more likely to exhibit academic success as compared to

48

Blacks and Hispanics. Further, Pascarella and Terenzini (2005) found that minority

students at Predominately-White Institutions felt isolated, which can be a factor that

negatively affects students’ success rates. For these students, one way to improve their

academic success lies with establishing a peer culture and orientation toward minority

issues (Pascarella & Terenzini, 1991). When considering ethnicity and residential status,

research suggests that minorities, especially Black students, are more likely to flourish in

a living-learning residential community than the White student majority (Pascarella &

Terenzini, 2005; Rodgers 2013). Currently, research does not examine the undergraduate

student attendance rates of different ethnicities; the research focuses only on academic

success rates.

The University of Southern Mississippi is classified as a Predominately-White

Institution (The Carnegie Classification of Institutions of Higher Education, 2016), but

the student body is 27% Black (Institutions of Higher Learning Board, State of

Mississippi, 2014). Because Black and White students comprise the majority of The

University of Southern Mississippi’s student body, there is not enough of a percentage to

provide confident data analysis for other specific ethnicities. A third category represents

the remaining ethnicities which include Asian, American Indian, Hispanic, Multi-Racial,

Not Specified, and Pacific Islander. As such, this study uses three different categories for

ethnicity: Black, White, and Other Ethnicity. The terms used to represent the ethnicities

coincide with the terminology used at the university.

Current Enrollment Status

To receive federal financial aid, students enrolled in college need to have at least

a full-time course load—a minimum of 12 credit hours (Federal Student Aid, An Office

49

of the U.S. Department of Education, 2016). In addition, only full-time students receive

the benefits of most university-based scholarships, such as out-of-state tuition waivers (E.

Dornan, personal communication, 2016). Classifying a student as full- or part-time is an

important factor of the present study because it determines whether there is a significant

difference in academic success between the groups. Students who enroll in 12 credit

hours or more a semester are classified as full-time, while students who are enrolled in

less than 12 credit hours a semester are classified as part-time. Kuh, Cruce, Shoup, and

Kinzie (2008) found that there is a significant difference in the academic success rates

between these groups. Crede et al. (2010) found a difference in the undergraduate

student attendance rates based on their course load. The Institutions of Higher Learning

(IHL) Institutional Report (2014) states that only 66.8% of full-time students at The

University of Southern Mississippi complete 24 credit hours in one academic year

compared to the 70.5% average for the entire IHL system. The report additionally states

that 37.9% of part-time students at The University of Southern Mississippi complete 12

credit hours within one academic year compared to the IHL system average of 44.3%

(Board of Trustees of State Institutions of Higher Learning, 2014). Therefore, reviewing

students’ success rates in conjunction with attempted course credit hours provides

additional relevant data for the present study.

University Designation

Universities receive different designations based upon the makeup of their

campuses (The Carnegie Classification of Institutions of Higher Education, 2016). Some

of these designations are commuter, residential, research, public, or private.

Additionally, a university may have a classification type based upon the university’s

50

history and ethnic makeup, leading to designations of a Predominately-White Institution

or a Historically Black College or University (The Carnegie Classification of Institutions

of Higher Education, 2016). If at least 25% of degree-seeking students live on campus or

nearby, a university is primarily a residential institution (The Carnegie Classification of

Institutions of Higher Education, 2016). The University of Southern Mississippi is

classified as a primarily residential, predominately-White, research university. The

University of Southern Mississippi’s designation is an important factor to consider

because the results may be generalizable to other institutions of a similar designation and

ethnic makeup.

Chapter Summary

In summary, existing literature discusses the many characteristics that influence

academic success through student retention. However, none of the previously mentioned

research combines multiple undergraduate student demographic factors affecting

attendance in the same study. The present study seeks to analyze the effects of an

electronic attendance monitoring system at one institution, The University of Southern

Mississippi, to provide usable data to enhance academic success. Through implementing

the electronic attendance monitoring system, the study aims to uncover data that policy

makers can use to influence the student body. In the end, the present study can serve as a

resource allowing other universities to make informed decisions regarding electronic

attendance monitoring systems.

In 2011, the majority of adults in the United States aged 25-34 did not have a

bachelor’s degree, and in the state of Mississippi, more than 71% did not have that level

of education (Education Commission of the States, 2011; Organization of Economic Co-

51

operation and Development, 2014). Mississippi’s eight publicly funded institutions of

higher learning have an average six-year graduation rate of 50% (Institutions of Higher

Learning Board, State of Mississippi, 2014). In response to years of lagging graduation

rates, the Institutions of Higher Learning reconfigured the funding method to reward

schools that focus on academic success, retention, and graduation (Institutions of Higher

Learning Board, State of Mississippi, 2013). The present study combines demographics

with data on undergraduate student attendance rates to discover traits that influence

academic success.

52

CHAPTER III – RESEARCH DESIGN AND METHODOLOGY

This study seeks to determine the relationship between undergraduate student

class attendance and academic success when employing an electronic attendance

monitoring system. In the Spring 2015 Semester, The University of Southern

Mississippi’s Office of Institutional Research monitored undergraduate student

attendance in certain course sections whose faculty voluntarily participated. While other

universities such as The University of Mississippi, University of Glamorgan (United

Kingdom), Loyola University of New Orleans, and The University of Northern Arizona

have used formalized attendance-monitoring procedures, this study fills a gap in the

research (Dicle & Levendis, 2013; Lopez-Bonilla & Lopez-Bonilla, 2015; Newman-Ford

et al., 2008; Newsom, 2016; O’Connor, 2010). Unlike previous studies, the present study

seeks to interpret the impact of an electronic attendance monitoring system on multiple

undergraudate student demographic factors affecting attendance.

The researcher uses archival data from The University of Southern Mississippi in

a quasi-experimental design (Goodwin, 2009; Shadish et al., 2002). The archival data

request was made for student demographic information from the Spring 2015 Semester

that, based on previous research studies, are important to attendance and academic

success (Church, 2002; Shadish et al., 2002). Appendix B contains a pre-authorization

letter approving the use of archival data from the organization. This chapter discusses the

research objectives, the design, a description of the population, and sampling procedures.

The methodology discusses the instrumentation, data collection procedures, validity and

reliability, and data analysis. Finally, the study’s limitations and chapter summary

complete the chapter.

53

Research Objectives

The purpose of the present study is to determine the influence of an electronic

attendance monitoring system on undergraduate student success. The link between

college academic success and human capital development appears when one examines

student development theory and human capital development principles. Four research

objectives guide this study:

RO1 - Describe the literature-based undergraduate demographic factors,

attendance rates, and academic success rates of the student sample.

RO2 - Compare academic success rates of students in the sections of courses

using the electronic attendance monitoring system in the Spring 2015 Semester with the

sections of courses that did not use the system.

RO3 - Determine the relationship between undergraduate attendance rates and

academic success in courses using an electronic attendance monitoring system.

RO4 - Determine the relationship between the literature-based undergraduate

demographic factors and attendance rates on student academic success in courses using

an electronic attendance monitoring system.

Research Design

This study analyzes archival data using a quasi-experimental design due to lack of

random assignment because the researcher was unable to assign subjects to specific

groups (Field, 2013; Goodwin, 2009; Shadish et al., 2002; Trochim 2006). The

researcher requested archival demographic data on the students enrolled in the course

sections that participated in the electronic attendance monitoring system and for students

enrolled in similar course sections that did not participate. The information includes the

54

following data: the number of students enrolled in each course, individual attendance

rates, individual end-of-course grades, and undergraduate student demographic factors

that relate to attendance and student success (gender, local residency, state of residency,

Greek Life organization affiliation, admission type, cumulative GPA, age, undergraduate

year classification, ethnicity, current enrollment status). Figure 3 illustrates the

difference between sections of non-participating and participating courses and shows how

the research objectives use the data.

55

Figure 4. Research design of the current research project

Spring 2015

Semester

Input: Students

Enrolled in

Selected Courses

with Intervention

Spring 2015

Semester

Result: Student

Success Rates

Spring 2015

Semester

Intervention: ID

Card Swiping for

Attendance

Spring 2015

Semester

Input: Students

Enrolled in

Selected Courses

w/o Intervention

Spring 2015

Semester

Result: Student

Success Rates

Student Enrolls in Academic Course: RO 1

Results: RO 2, 3, 4

56

Population and Sample

The present research study took place at The University of Southern Mississippi,

where six out of ten undergraduate college students, who begin as freshman, do not

graduate within four years (Institutions of Higher Learning Board, State of Mississippi,

2013). The University’s Office of Institutional Research implemented an electronic

attendance monitoring system during the Spring 2015 Semester. The researcher

requested the data from that semester as well as academic success data and demographic

factors of the population and sample.

The population for this study was undergraduate students enrolled during the

Spring 2015 Semester at The University of Southern Mississippi. Additionally, the

electronic attendance monitoring system took place in regular session course sections; no

honors course sections, online course sections, offsite course sections, or abbreviated

term course sections participated. The sample includes students from the course sections

whose faculty voluntarily participated in the electronic attendance monitoring system and

students in the course sections that did not participate. Because the study uses archival

data, the opportunity to pre-select students for participation did not exist. The students

choose the courses and section based on personal schedules and preferences. Because the

students were not randomly assigned to any of the courses, no controls are present to

ensure that the same demographics of students enroll in each course (Goodwin, 2009).

Previous Academic Success of Sample

The study compares data among students in the Spring 2015 Semester of the

courses and sections using the electronic attendance monitoring system to data for

students who were in similar courses and sections, but who did not use the electronic

57

attendance monitoring system. Three lecture-based courses voluntarily participating in

the electronic attendance monitoring system and have comparable class sections that did

not take part:

• History 101 (World Civilization I)

• History 102 (World Civilization II)

• Math 102 (Brief Applied Calculus)

Table 2 describes the relevant academic success summary data from the period of Fall

2009 to Fall 2014 (prior to implementing the electronic attendance monitoring system)

for the courses that participated in the electronic attendance monitoring system. The

table outlines how many students have taken the course during the Fall 2009 to Fall 2014

period and the academic success rates of the enrolled students.

Table 2

Academic Success in Electronic Attendance Monitoring System Participating Courses

Success Not Success

Course Description Total

C or Better

n (%)

Less than C

n (%)

Withdrew

n (%)

DFW

n (%)

History 101

World Civilization I 9,775

5,401

(54.6%)

3,992

(41.6%)

382

(3.9%)

4,374

(45.5%)

History 102

World Civilization II 8,184

5,090

(62.3%)

2,818

(34.4%)

276

(3.3%)

3,094

(37.7%)

Math 102

Brief Applied Calculus 1,772

1,038

(58.8%)

650

(36.7%)

84

(4.5%)

734

(41.2%)

Note. The total represents the total number of students enrolled in the course between the fall 2009 and fall 2014 semesters. The C or better column represents the number of students and the percentage of total students enrolled between the fall 2009 and fall 2014

semesters who achieved an academic grade of a C or better (A, B, or C). The Less than C column represents the number of students

and the percentage of total students enrolled between the fall 2009 and fall 2014 semesters who achieved an academic grade of a C or better (D or F). The Withdrew column represents the number of students and the percentage of total students enrolled between the fall

2009 and fall 2014 semesters who withdrew from the course while attempting to complete it (the student did not complete the course and did not receive any course credit). The DFW column represents the combined number of students and the percentage of total

students enrolled between the fall 2009 and fall 2014 semesters who achieved an academic grade of a D or F or if the student

withdrew from the course. If the DFW rate is above 30% for six of the eight past semesters, the course is considered historically

difficult.

58

Table 3 outlines the courses participating in the electronic attendance monitoring

system and presents the number of participating and non-participating course sections

during the Spring 2015 Semester.

Table 3

Electronic Attendance Monitoring System Participating Courses

Course Name

Total Sections

(2015)

Electronic Attendance

Monitoring System Sections

(2015)

History 101 (World Civilization I) 3 2

History 102 (World Civilization II) 3 1

Math 102 (Brief Applied Calculus) 7 2

Sampling Process

Prior to implementing the electronic attendance monitoring system project,

Institutional Research staff contacted certain faculty to solicit participation, explain

benefits of participation, and the outcomes that could occur because of their participation

(M. Arrington, personal communication, 2015). The participants were chosen based on

the available academic success data from the Institutional Research office (M. Arrington,

personal communication, 2015). Through their voluntary participation, the faculty for the

course determined the students to participate in the electronic attendance monitoring

system (M. Arrington, personal communication, 2015). Because of the recruitment style

of the faculty for the electronic attendance monitoring system, the sampling process

utilizes convenience sampling (Field, 2013; Shadish et al., 2002). Convenience sampling

represents a type of non-probability sampling where the subjects are selected because of

their accessibility (Field, 2013; Shadish et al., 2002).

59

Instrumentation

During the electronic attendance monitoring system trial, the Office of

Institutional Research used the MagTek Model-21040145 SureSwipe Dual Head Triple

Track Magnetic Stripe Card Reader with 6' universal serial bus (USB) Interface Cable, 60

in/s Swipe Speed, 5V, to collect data. The project used 10 identification card swipe

machines to collect data. The card swipe machines accommodate a university

identification card swiped facing any direction which prevented having to align the

identification cards a certain way to capture their information. The Office of Institutional

Research selected the MagTek card swipe machines for their compatibility with the

current student identification cards (M. Arrington, personal communication, 2015). In

addition, it meant students enrolled in the classes where the electronic attendance

monitoring system occurred would not have to carry around an additional item to verify

their attendance. The system offers the flexibility to manually input a student

identification number. The staff members who collected the data would use the card

swipe machines to track attendance and view the computer screen to ensure that the

student identification card registered correctly (M. Arrington, personal communication,

2015). Using the MagTek card swipe machine presents an important difference from

other universities because the system did not require the installation of expensive

equipment, incorporation of new radio frequency identification detectors, or barcode

scanners (M. Arrington, personal communication, 2015). Using the card swipe machine

allowed for rapid data collection.

60

Data Collection

According to the director of the Office of Institutional Research, the project began

with members of the Office of Institutional Research staff volunteering to ensure accurate

data collection (M. Arrington, personal communication, 2015). The protocol developed

by the Office of Institutional Research staff, and vetted through the faculty, stated that the

seven staff members tasked with swiping identification cards would ensure card readers

were available 15 minutes prior to the class beginning and remained there until five

minutes after the class began (M. Arrington, personal communication, 2015). Data

collectors were obligated to ensure that they had a full view of the computer screen to

reduce errors. If a student forgot to bring their student identification card, the system

allowed for the manual input of their numbers. All data, swiped and manually entered,

was uploaded in the same file to the Institutional Research database. A byproduct of the

electronic attendance monitoring system was that it created more time for instruction in

the class, as the faculty did not need to collect attendance information prior to every class

period, as compared with faculty who devoted time to record attendance themselves (M.

Arrington, personal communication, 2015). There was a possibility that non-enrolled

students could swipe into the monitored course, but the cross-referencing of the swiped

data with the class roster ensured that non-enrolled students’ information did not end up

in the database.

The staff members operated their location beginning 15 minutes prior to the class

start time to five minutes after the class started (M. Arrington, personal communication,

2015). The presence of a person at the check-in sites ensured that the course could start

on time and allowed for some members of the course to arrive late while still being

61

included in the figures collected. As the semester progressed, three student workers were

hired to relieve some staff members of having to attend two or three classes to complete

the project. For large classes (those with over 120 registered students) two people staffed

the location to collect data to ensure quick entry to the classroom. On some days, only

one staff member was present, but the difference in staff did not affect the quality of the

data collected or starting time of the class (M. Arrington, personal communication, 2015).

Upon entering the university, students submit demographic data that updates as

they matriculate (M. Arrington, personal communication, 2015). The researcher

requested archived demographic information from students’ online records supplied by

the Office of Institutional Research. The accuracy of the data relies on the student’s

attention to detail. The university regularly updates some of the data, like undergraduate

year classification, throughout the time that a student is enrolled. These records are

accessible through the online record keeping software used by The University of

Southern Mississippi, PeopleSoft. Faculty, staff, and students know the system as SOAR

or Southern’s Online Accessible Records.

Institutional Review Board

Upon completion of the dissertation proposal defense, the researcher submitted

the research proposal to The University of Southern Mississippi’s Office of Research

Integrity’s Institutional Review Board (IRB) for approval. The application includes a

detailed explanation of the proposed research, ways to ensure the privacy of subjects, and

how confidentiality of all the data would be maintained. The Institutional Review Board

ensures that researchers take appropriate safeguards to protect any subjects involved,

protect the data researchers use, and to ensure that the research complies with applicable

62

federal and institutional standards and guidelines (The University of Southern

Mississippi, n.d.). The official approval from the Institutional Review Board is provided

in Appendix C.

Data Request and Confidentiality of Data

The Office of Institutional Research granted approval to use the electronic

attendance monitoring system data as evidenced in the approval letter found in Appendix

B. To request the data, the researcher completed an Institutional Review Board (IRB)

request. One of the most important parts of this data request is to ensure the

confidentiality of the data. The researcher asked that the data not include student names

or identification numbers to protect student confidentiality. To support confidentiality,

the researcher requests that the data use the following identification method: Course

Name-Section Number-Year-Roster Number (ex. HIS101;H001;2015;45). Further, each

semicolon (;) represents separated data in Microsoft Excel in a different cell in the table

so that it assists in the data analysis process. Labeling the data in this fashion allows for

the analysis of data at a later point. Academic success metrics from the Spring 2015

Semester include the total number of students enrolled in each section and individual

end-of-course grade. For the courses taking part in the Spring 2015 electronic attendance

monitoring system, the researcher requested individual attendance rates (attended/max

number of class meetings) and undergraduate demographic factors. The researcher

requested that the demographic factors be coded as follows: Gender (Male/Female), local

residency (On-Campus/Off-Campus), state of residency (Mississippi/Other), Greek Life

organization affiliation (Yes/No), admission type (Freshman/Transfer), cumulative GPA

at time of course enrollment (Below 2.5/2.5 and above), age at the time of course

63

enrollment, undergraduate year classification (Freshman, Sophomore, Junior, Senior),

ethnicity (American Indian, Asian, Black, Hispanic, Multiracial, Not Specified, Pacific

Islander, White), and current enrollment status (Part-Time, Full-Time) during the Spring

2015 Semester (Part-Time/Full-Time). Coding the undergraduate student attendance-

based demographics in this fashion allows for the ease of analysis.

Data Analysis

Upon authorization from The University of Southern Mississippi’s Institutional

Review Board to proceed, the researcher requested the selected archival data from The

University of Southern Mississippi Office of Institutional Research. The information was

requested in a format that allows for a seamless transition to IBM’s Statistical Package

for the Social Sciences (SPSS) software Version 22, such as a Microsoft Excel file.

Table 4 illustrates the timetable for the occurrence of the data analysis. Once

approved by the Institutional Review Board, the researcher submitted a data request. The

researcher then utilized the next weeks to code the data in SPSS. Upon completion, the

following timetable provided a timely guide.

Table 4

Data Analysis Timetable

Week Task

Week Zero Approval received from the Office of Research Integrity.

Weeks One - Three Sent a request for archival data to the Office of

Institutional Research.

Week Three Upon receipt of archival data from the Office of

Institutional Research, imported data into IBM’s SPSS to

run statistical analysis tests.

64

Table 4 (Continued)

Week Task

Weeks Four - Seven Analyzed data pertaining to RO 1 - Describe the

undergraduate demographic factors affecting attendance,

academic success rates, and attendance rates of the student

sample.

Analyzed data pertaining to RO 2 - academic success rates

of students in the sections of courses using the electronic

attendance monitoring system in the Spring 2015 Semester

with the sections of courses that did not use the system.

Analyzed data pertaining to RO 3 - Determine the

relationship between undergraduate attendance rates and

academic success when using an electronic attendance

monitoring system.

Analyzed data pertaining to RO 4 - Determine the

relationship between the undergraduate demographic

factors affecting attendance and attendance rates on

student academic success when using an electronic

attendance monitoring system

Weeks Eight - Ten Completed established tables with report of results from

SPSS and summarize results.

Data Analysis Plan

The following section outlines the data analysis plan and explains the different

types of data used in the study. As previously outlined, the researcher requested the data

upon IRB approval. The Office of Institutional Research provided data in three category

types: nominal, ordinal, and ratio. Nominal data are labels for variables that have no

numeric value (Field, 2013; Shadish et al., 2002). Ordinal data does have an order in the

values, but the differences separating the values are unknown (Field, 2013; Shadish et al.,

2002). Ratio-based data tells the researcher the exact value of the data points and has an

absolute zero-based figure (Field, 2013; Shadish et al., 2002).

65

To analyze RO1, the researcher describes the demographics of the population and

sample, student success rates, and undergraduate student attendance rates, using

descriptive statistics. Tables and figures describe the data which include numbers,

percentages, and histograms. Some tables show the differences in academic success rates

between the sections of courses using the electronic attendance monitoring system and

the sections of courses that did not use the system.

Logistic regression was used to analyze RO2 and RO4. Logistic regression

analysis allows the researcher to use the demographic variables as predictors to estimate

the likelihood of an event to occur (Field, 2009; Gellman & Hill, 2007; Shadish et al.,

2002; Wong & Mason, 1985). In this case, the event observed is the student’s record of

attendance in the classroom. Logistic regression also allows the researcher to analyze the

impact of demographic variables on a student’s decision to attend class (Peng & So,

2002). The researcher chose logistic regression because the student demographic data

categories and subcategories for classes can accommodate dependent variables that are

dichotomous, categorical, and allow for the use of dummy variables as the independent

variable, something that linear regression testing cannot perform (Field, 2009; Gellman &

Hill, 2007; Shadish et al., 2002; Wong & Mason, 1985). Additionally, Peng and So

(2002) believe demographics must be analyzed to determine if one variable interacts with

another as a risk factor affecting attendance and a logistic regression analysis examines

the relationship. The continuous variable for Research Objective 2 is attendance rates.

The dependent variable presents the academic success rates of the sample. The basic

assumptions for logistic regressions are For logistic regression analysis some basic

assumptions exist: (a) having a dichotomous dependent variable, (b) ensuring that each

66

grouping is unique meaning that there can be no crossover of data within a group, (c)

large sample sizes are needed in order for the data to be robust, and (d) that a linear

relationship does not exist between the independent and dependent variables (Field, 2009;

Gellman & Hill, 2007; Shadish et al., 2002; Wong & Mason, 1985). Another assumption

of the logistic regression is that the variables are measured accurately (Field, 2009;

Gellman & Hill, 2007; Shadish et al., 2002; Wong & Mason, 1985). One item that may

stand out due to accuracy is the presence of outliers. As such, major outliers in the data

should be removed to prevent skewing the data (Field, 2009; Gellman & Hill, 2007;

Shadish et al., 2002; Wong & Mason, 1985). A 95% confidence level (p < .05), was used

in these tests.

Additionally, an odds ratio is reported in a logistic regression. The odds ratio

statistic, eB, provides an explanation describing the relationship between the independent

and dependent variables (Field, 2009; Peng & So, 2002; Shadish et al., 2002; Statistic

Solutions, 2013). The odds ratio describes the change in odds of being in a category of

the dependent variable for every unit increase of any given variable in the logistic

regression model output (Field, 2009; Peng & So, 2002; Shadish et al., 2002; Statistic

Solutions, 2013). In logistic regressions, if the odds ratio is above 1, it shows the

importance of the variable to academic success (Field, 2009; Peng & So, 2002; Statistic

Solutions, 2013). Conversely, if the eB figure is less than 1, it indicates that an inverse

relationship exists (Field, 2009; Peng & So, 2002; Statistic Solutions, 2013). For a less

than 1 eB figure, as the independent variable increase, the less likely for the outcome to

occur (Field, 2009; Statistic Solutions, 2013).

67

The researcher uses a point-biserial statistical test to evaluate Research Objective

3 and the supposition that students who attend have better academic success rates than

students who do not (Field, 2009; Shadish et al., 2002). The point-biserial tests used a

95% confidence interval (p < .05) for all tests, which is the norm for all social science

statistics (Field, 2009; Shadish et al., 2002). Point-biserial tests report a Pearson

coefficient number in the output. The Pearson coefficient numbers can range from -1 to

1; where -1 represents a perfect negative relationship and 1 represents a perfect positive

relationship (Field, 2009; Statistic Solutions, 2013). A perfect negative relationship

means that there is an indirect relationship between the variables and as one variable

increases the other variable will decrease (Field, 2009; Statistic Solutions, 2013). A

perfect positive relationship means that there is a direct relationship between the variables

and as one increases so does the other (Field, 2009). Cohen’s standard segments

correlations into distinct categories where .10 to .29 represents a weak association, .30 to

.49 represents a moderate association, and .50 and above represents a strong association

(Statistic Solutions, 2013). By squaring the Pearson coefficient number, researchers

determine is the percentage of variability that one factor has on the other (Field, 2009;

Statistic Solutions, 2013). Point-biserial tests have certain assumptions to ensure validity.

Point-biserial tests must have one or two variables using ratio data, while the other

variable is dichotomous (Field, 2009). Point-biserial testing does not use independent

and dependent variables because it is a test comparing two variables to each other (Field,

2009). This research objective uses the point-biserial test to assess the importance of

undergraduate student attendance on academic success.

68

Table 5 presents the data analysis plan for the present research project. The table

is divided to illustrate the four research objectives. Table 5 also identifies the statistical

tests used for each research objective, the data types, and, the independent and dependent

variables.

Table 5

Data Analysis Plan

Research Objective Statistical Test Data Category Variables

RO1 – Describe

Student Demographics,

Academic Success

Rates, and Student

Attendance Rates

Descriptive

Statistics –

Nominal, Ordinal,

Ratio

Frequencies and

Percentages

RO2 – Difference in

academic success when

using electronic

attendance monitoring

Logistic

Regression

Nominal and

Ratio

IV: Attendance-

monitoring rates

DV: Academic

Success

RO3 – Attendance rates

relationship to

academic success in

participating courses

Point-biserial

correlation

Nominal and

Ratio

Continuous Variable:

Attendance-

monitoring rates

Dichotomous

Variable: Academic

Success

RO4 – Influence of

Demographics and

Attendance on

Academic success

Logistic

Regression

Nominal and

Ratio

IV: Demographics,

Continuous Variable:

Attendance-

monitoring rates

DV: Academic

Success

Note. The Demographic variables referenced in the research objectives are gender, local residence, state residence, Greek affiliation, admission type, cumulative GPA, age, classification, ethnicity, and enrollment status. Independent variable is represented as IV.

Dependent variable is represented as DV.

Validity and Reliability

In any research study, validity and reliability are two key factors. Validity

ensures the present research is appropriate, meaningful, and that the researcher’s

69

conclusion can be useful (Fraenkel & Wallen, 2006). Additionally, validity breaks down

into two subordinate subsets, internal and external validity, and determines if the

requirements of the research methods are met (Shadish et al., 2002). External validity

refers to whether or not the results and generalizations are applicable to a larger

population (Fraenkel & Wallen, 2006; Shadish et al., 2002). External validity was

controlled because the population involved all students from courses participating, and

the sections that did not participate, in the electronic attendance monitoring system

(Fraenkel & Wallen, 2006; Shadish et al., 2002). Internal validity is addressed because

course sections that did not employ the electronic attendance monitoring system are in

the data received and used as a control group (Fraenkel & Wallen, 2006; Shadish et al.,

2002). By doing this, the researcher can control the presence of other reasons that may

affect the outcomes of the tests (Fraenkel & Wallen, 2006; Shadish et al., 2002).

Reliability defines the degree of dependability of the assessment tool (Fraenkel &

Wallen, 2006; Shadish et al., 2002). The Office of Institutional Research organized the

electronic attendance monitoring system and thus ensuring reliability of the data

collection rested with their staff. Members of the office staff conducted training for all

individuals tasked with collecting data (M. Arrington, personal communication, 2015).

The protocol consisted of having a training session to demonstrate the proper collection

procedures and a member of the staff being present to observe the person during the first

day of swiping in their assigned course (M. Arrington, personal communication, 2015).

The protocol established a system ensuring that individuals knew what to expect during

their assigned times to swipe for a course and no issues arose to indicate a gap in

reliability (M. Arrington, personal communication, 2015).

70

Chapter Summary

In summary, the present study seeks to determine the influence of an electronic

attendance monitoring system on undergraduate student success. Chapter III presents

information regarding research objectives, research design, population, sample, sampling

process, instrumentation, data collection, research objectives, data analysis, and

limitations of the study. This study utilizes a quasi-experimental design using archival

data. Data was analyzed for the Spring 2015 Semester when the electronic attendance

monitoring system was implemented. In Chapter III the methodology was described to

include the chosen statistical tests determined by the research objectives. The data

analysis occurred using IBM SPSS version 22 software. Chapter IV summarizes the

results and analysis of the study.

71

CHAPTER IV – RESULTS

The present study determined the influence of an electronic attendance monitoring

system on undergraduate student success. Chapter IV reports the results of the study’s

research objectives which determined the design of the study and the process for

analyzing the data. This study utilizes a quasi-experimental design using archival data

from an electronic attendance monitoring system implemented during the Spring 2015

Semester. The analysis for the study includes descriptive statistics, logistic regression,

and a point-biserial correlation. The sample includes University of Southern Mississippi

undergraduate students from the Spring 2015 course sections whose faculty voluntarily

participated in the electronic attendance monitoring system.

Research Objective 1 – Describing demographics, attendance, and academic success rates

Research Objective 1 describes the demographic factors, attendance rates, and

academic success rates of the student sample. Research Objective 1 uses descriptive

statistics to provide a snapshot of the population and sample. Descriptive statistics

provide quantitative data to researchers using simple tables, graphics, and summary data

(Field, 2009; Shadish et al., 2002). The student demographics, academic success rates,

and student attendance data were obtained from the Institutional Research Office.

Demographic Characteristics

The research-based demographic factors, tying both to academic success and

attendance accountability, include gender, local residence, state residence, Greek

affiliation, admit type, cumulative GPA, age, undergraduate classification, ethnicity, and

enrollment status. The demographics in Table 6 describe the population and sample in

the present study. During the Spring 2015 semester, the total number of students in the

72

sample is 1593, where 640 students were enrolled in sections of courses using the

electronic attendance monitoring system and 953 students were enrolled in sections of

courses that did not use monitor attendance. Table 6 presents the demographic

information of students in the sections of courses using the electronic attendance

monitoring system and the sections of courses that did not use the system.

Faculty volunteered to use the electronic attendance monitoring system and were

not randomly selected. Student demographic information was collected as students

applied for admission to the university and updated throughout a student’s enrollment.

Table 6

Group Demographic Characteristic Comparison

Characteristics

Attendance Monitoring

Number (%)

Non-Attendance Monitoring

Number (%)

Gender

Female 388 (60.6%) 610 (64.0%)

Male 252 (39.4%) 343 (36.0%)

Local Residence

Commuter 257 (40.2%) 593 (62.2%)

Resident 383 (59.8%) 360 (37.8%)

State Residence

In-State 466 (72.8%) 784 (82.3%)

Out-of-State 174 (27.2%) 169 (17.7%)

Greek Affiliation

No 451 (70.5%) 770 (80.8%)

Yes 189 (29.5%) 183 (19.2%)

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Table 6 (Continued)

Characteristics

Attendance Monitoring

Number (%)

Non-Attendance Monitoring

Number (%)

Admit Type

Freshman 534 (83.4%) 590 (61.9%)

Transfer 106 (16.6%) 361 (37.9%)

Cumulative GPA

Below 2.5 397 (62.0%) 470 (49.3%)

2.5 and Above 211 (33.0%) 373 (39.1%)

No Established GPA 32 (5.0%) 110 (11.5%)

Age

Traditional (18-24) 609 (95.2%) 765 (80.3%)

Non-Traditional (>24) 31 (4.8%) 188 (19.7%)

Classification

Underclassmen 521 (81.4%) 589 (61.8%)

Upperclassmen 119 (18.6%) 364 (38.2%)

Ethnicity

American Indian 2 (0.3%) 6 (0.6%)

Asian 10 (1.6%) 16 (1.7%)

Black 230 (35.9%) 378 (39.7%)

Hispanic 27 (4.2%) 34 (3.6%)

Multiracial 14 (2.2%) 14 (1.5%)

Not Specified 1 (0.2%) 12 (1.3%)

Pacific Islander 1 (0.2%) 2 (0.2%)

White 355 (55.5%) 491 (51.5%)

Enrollment Status

Part-Time 15 (2.3%) 82 (8.6%)

Full-Time 625 (97.7%) 871 (91.4%)

74

As students applied for admission, two gender choices were available, female or

male. At the time, the university did not provide additional choices for gender

identification. During the semester of study, the gender breakdown at the university level

for the population was 63.2% female and 36.8% male. In the sections of courses that

instituted the electronic attendance monitoring system, 60.6% (n = 388) were female and

39.4% (n = 252) were male, representing a 21.2% higher rate of females than males. The

sections of courses that did not use the electronic attendance monitoring system was

64.0% (n = 610) female and 36.0% (n = 343) male.

Each semester, students make an important choice regarding their desire to reside

in on-campus housing or to reside off-campus and be a commuter student. Information

regarding the residency designation for the university population was not available

through an online search for the information nor was it provided through the request for

archival data. Overall, the students enrolled in the sections of courses using the

electronic attendance monitoring system had a makeup of 40.2% (n = 257) commuter

students and 59.8% (n = 383) resident students. Comparatively, the demographics of

students not in the sections of courses using the electronic monitoring system were made

of 62.2% (n = 593) commuter students and 37.8% (n = 360) resident students.

Universities across the nation recruit students from both their home state and

other states in the United States, as well as other countries. For the purposes of the study,

students were categorized as either being in-state residents or out-of-state residents. The

population of the university is made up of 65.0% in-state residents and 35.0% out-of-state

residents. The sections of courses using the electronic attendance monitoring system had

a state residence breakdown of 72.8% (n = 466) as in-state residents and 27.2% (n = 174)

75

as out-of-state residents. In the sections of courses that did not monitor attendance 82.3%

(n = 784) were in-state residents and 17.7% were out-of-state residents.

The literature review presented research conducted regarding the success of

students if they were involved in at least one major campus organization. In this study,

the major campus organization selected was whether a student is involved in a fraternity

or sorority. Information regarding the number of students university-wide involved in

Greek Life is not available on the Institutional Research website, nor was it provided

through the data request. However, in the sample, the number of students not involved in

a Greek organization in the sections of courses using the electronic attendance monitoring

system was 70.5% (n = 451) and the number of students who were in a Greek

organization was 29.5% (n = 189). In the sections of courses that did not participate in

the electronic attendance monitoring system the number of non-Greek affiliated students

was 80.8% (n = 770) and the number affiliated with a Greek organization was 19.2% (n =

183).

As students are admitted to the university, they receive a classification as either a

freshman admit, their first time in college, or a transfer admit, those students transferring

credit from another institution. The University of Southern Mississippi publishes the

number of new students each semester who fall into these categories, however, the

figures are not available for the full university-wide population. The sections of courses

that took part in the electronic attendance monitoring system had 83.4% (n = 534)

freshman admit students and 16.6% (n = 106) transfer admit students. Comparatively,

the sample included 61.9% (n = 590) freshman admit students and 37.9% (n = 361)

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transfer admit students in the sections of courses that did not participate in the electronic

attendance monitoring system.

As students progress through college, their academic success is recorded as a

Cumulative GPA, another demographic factor used in this study. The GPA was recorded

with three distinct categories, students who earn Below a 2.5 Cumulative GPA, students

who achieve a 2.5 and Above, and students with no previous university grade point

average. No figures are available to describe the student population for this demographic

because the information is not published. In the sections of courses implemented the

electronic attendance monitoring system, 62.0% (n = 397) of the students in the sample

had a GPA below a 2.5, 33.0% (n = 211) had a GPA of 2.5 and above, and 5.0% (n = 32)

were without any previous university GPA. In the sections of courses that did not take

place in the electronic attendance monitoring system, 49.3% (n = 470) earned a GPA

below a 2.5, 39.1% (n = 373) attained a GPA of 2.5 and above, and 11.5% (n = 110) were

without any previous university GPA.

Colleges and universities find a wide age range of students in attendance pursuing

a degree. Traditional aged students are defined as being between 18-24 years of age and

those above 24 years of age are defined as non-traditional students. In all, the university

population for this demographic at the time was 67.5% in the traditional age and 32.5%

in the non-traditional age group. Sections of courses that used the electronic attendance

monitoring system had a makeup of 95.2% (n = 609) traditional aged students and 4.8%

(n = 31) non-traditional aged students. The sections of courses that did not use the

electronic attendance monitoring had a breakdown of 80.3% (n = 765) traditional aged

and 19.7% (n = 188) in the non-traditional category. The difference in the percentages

77

can be attributed to the courses that took part in the electronic attendance monitoring

system and their place in the course sequencing for academic majors which is to enroll in

these classes early in the undergraduate career.

As students progress through college, they are classified in one of four categories,

freshman, sophomore, junior, or senior. The present study used two factors to classify

students, underclass students which are freshman and sophomores, and upperclass

students which are juniors and seniors. Altogether, the classification of the student body

population for this demographic factor shows that 38.6% of the university-wide students

are classified in the underclass category and 61.4% in the upperclass category. In the

sections of courses that used the electronic attendance monitoring system, the makeup

was 81.4% (n = 521) underclass students and 18.6% (n = 119) upperclass students. The

sections of courses that did not use the electronic attendance monitoring system consisted

of 61.8% (n = 589) underclass students and 38.2% (n = 364) upperclass students.

Students have the opportunity to select their ethnicity when applying for entrance

into the university. During this process, individuals can select one of eight categories to

describe their ethnicity which are American Indian, Asian, Black, Hispanic, Multiracial,

Not Specified, Pacific Islander, or White. Due to the lack of numbers in each ethnicity

category in the courses that used the electronic attendance monitoring system, some

ethnicities had to be combined for the purposes of conducting the research. The ethnicity

for the population was 62.9% in the White category, 27.2% in the Black category, and

9.9% in the combined other ethnicity category. The ethnicity of students in the sections

of courses that used the electronic attendance monitoring system is 55.5% (n = 355) in

the White category, 35.9% (n = 230) in the Black category, and 8.6% (n = 55) in the

78

combined other ethnicity category. The ethnicity of students in the sections of courses

that did not use the electronic attendance monitoring system is 51.5% (n = 491) in the

White category, 39.7% (n = 378) in the Black category, and 8.9% (n = 84) in the

combined other ethnicity category.

Each semester, students are able to choose how many credit hours they wish to

enroll as they select their classes. Full-time students must be enrolled in at least 12 credit

hours while individuals below 12 credit hours are classified as part-time students.

Overall, the enrollment status breakdown for the population was 11.9% part-time

students and 88.1% full-time students. The breakdown in the sections of courses that

used the electronic attendance monitoring system was 2.3% (n = 15) part-time students

and 97.7% (n = 625) full-time students. This compared to 8.6% (n = 82) part-time

students and 91.4% (n = 871) full-time students in the sections of courses that did not

utilize the electronic attendance monitoring system.

Electronic Attendance Monitoring System Descriptive Results

Research Objective 1 also presents the data on the percentage of class times that

students attended class. Research Objective 1 breaks down the data in two ways. Figure

5 presents the undergraduate student electronic attendance monitoring data combining all

participating courses in one graphic. Figure 5 uses descriptive statistics to show the

undergraduate student electronic attendance monitoring-data describing the attendance

rates for the individual class sections for the sample. Figure 5 organizes the attendance

data as follows: 0-20%, 20.1-40%, 40.1-60%, 60.1-80%, 80.1-90%, 90.1-95%, 95.1-

99.99%, and 100%. The data provided by the Office of Institutional Research was in

percentage of classes that the student attended. The percentage figures provide increased

79

flexibility of data usage as different course sections met on different days and for

different durations. Therefore, having the total percentage of class attended provided an

accurate way for the researcher to compare data between courses.

Figure 5. Combined Classroom Attendance Data

Through the usage of the electronic attendance monitoring system, the study

provides data that has previously been unknown and unable to analyze for statistical

testing. To note, each one of the histogram sections is not equal. The histograms used a

system that separated the attendance rates in 20% increments. However, because the

80.1% to 100% range had the highest number of people, which was unknown until the

data was received, this range was divided into four distinct categories to better illustrate

the attendance figures. The results from Figure 5 show that of all the students involved in

the electronic attendance monitoring system, 11.0% (n = 71) students attended class

1334

50

96

170

129

77 71

0

20

40

60

80

100

120

140

160

180

200

Combined Rates of Attended Class

Nu

mb

er o

f St

ud

ents

0-20% 20.1-40% 40.1-60% 60.1-80% 80.1-90% 90.1-95% 95.1-99.9% 100%

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meetings 100% of the time, 12.0% (n = 77) students attended 95.1 to 99.9% of class

meetings, 20.2% (n = 129) of students were in class for 90.1 to 95% of full meeting time,

26.6% (n = 170) of students attended class between 80.1 to 90% of available course

meetings, and 15.0% (n = 96) of students attended class 60.1 to 80% of class meeting

time. This shows that 84.8% (n = 543) students attended class 60% or more of the time.

In contrast, 15.2% (n = 97) students did not attend class over 60% of the time. That

breakdown is 2.1% (n = 13) of students attended class 0 to 20% of the time, 5.3% (n =

34) were in class for 20.1 to 40% of class meetings, and 7.8% (n = 50) attended class 40.1

to 60% of the time.

Figure 6 uses descriptive statistics to show the undergraduate student electronic

attendance monitoring-data describing the attendance rates for the individual class

sections for the sample. Figure 6 organizes the attendance data as follows: 0-20%, 20.1-

40%, 40.1-60%, 60.1-80%, 80.1-90%, 90.1-95%, 95.1-99.99%, and 100%.

The results from Figure 6 were broken down by course title enabling the

illustration of attendance in specific courses involved in the electronic attendance

monitoring system. In History 101, 6.7% (n = 19) students attended class meetings 100%

of the time, 13.6% (n = 39) students attended 95.1 to 99.9% of class meetings, 17.2% (n

= 49) of students were in class for 90.1 to 95% of full meeting time, 25.0% (n = 71) of

students attended class between 80.1 to 90% of available course meetings, and 16.5% (n

= 47) of students attended class 60.1 to 80% of class meeting time. This shows that

79.0% (n = 225) students attended class 60% or more of the time. In contrast, 21.0% (n =

60) students did not attend class over 60% of the time. That breakdown is 2.1% (n = 6)

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of students attended class 0 to 20% of the time, 7.0% (n = 20) were in class for 20.1 to

40% of class meetings, and 11.9% (n = 34) attended class 40.1 to 60% of the time.

Figure 6. Individual Course Attendance Monitoring Data

6 34

2013

1

34

12

4

47

40

9

71

8811

49

66

14

39

29

9

19

4012

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

History 101 History 102 Math 102

Per

cen

tage

of

Stu

den

ts in

Co

urs

e

Percentage of Attended Class

0-20% 20.1-40% 40.1-60% 60.1-80%

80.1-90% 90.1-95% 95.1-99.9% 100%

82

In History 102, 13.7% (n = 40) students attended class meetings 100% of the

time, 10.0% (n = 29) students attended 95.1 to 99.9% of class meetings, 22.7% (n = 66)

of students were in class for 90.1 to 95% of full meeting time, 30.2% (n = 88) of students

attended class between 80.1 to 90% of available course meetings, and 13.7% (n = 40) of

students attended class 60.1 to 80% of class meeting time. This shows that 90.3% (n =

263) students attended class 60% or more of the time. In contrast, 9.7% (n = 28) students

did not attend class over 60% of the time. That breakdown is 1.2% (n = 3) of students

attended class 0 to 20% of the time, 4.4% (n = 13) were in class for 20.1 to 40% of class

meetings, and 4.1% (n = 12) attended class 40.1 to 60% of the time.

In Math 102, 18.6% (n = 12) students attended class meetings 100% of the time,

14.1% (n = 9) students attended 95.1 to 99.9% of class meetings, 21.8% (n = 14) of

students were in class for 90.1 to 95% of full meeting time, 17.3% (n = 11) of students

attended class between 80.1 to 90% of available course meetings, and 14.1% (n = 9) of

students attended class 60.1 to 80% of class meeting time. This shows that 85.9% (n =

55) students attended class 60% or more of the time. In contrast, 14.1% (n = 9) students

did not attend class over 60% of the time. That breakdown is 6.3% (n = 4) of students

attended class 0 to 20% of the time, 1.5% (n = 1) were in class for 20.1 to 40% of class

meetings, and 6.3% (n = 4) attended class 40.1 to 60% of the time.

One area of Figure 6 that stands out is in History 101, the number of people who

were in the 40.1-60% range. Otherwise, the data shows that the percentage of student

attending class less than 60% of the time is below 15%. The attendance range where the

largest majority of students attended class is the 80.1-90% range for History 101 and

History 102 but is 90.1-95% for Math 102. History 101 has a very low 100% attendance

83

percentage when compared to the other courses in the study. Appendix D presents an

adapted view of Figure 6 in a table format.

Academic Success Rates

The final section of Research Objective 1 provides the summary information

regarding academic success rates in the Spring 2015 Semester in the sections of courses

using the electronic attendance monitoring system and the sections of courses that did not

use the system. Table 7 presents a breakdown of students, on a summary scale and

categorized by each course, in the sections of courses using the electronic attendance

monitoring system and the sections of courses that did not use the system during the

Spring 2015 Semester.

Table 7

Breakdown of Students in Participating and Non-Participating Courses

Course

Attendance

Monitoring

(n)

Non-Attendance

Monitoring

(n) Total

All Courses Combined 640 953 1593

History 101 World Civilization I 285 219 504

History 102 World Civilization II 291 540 831

Math 102 Brief Applied Calculus 64 194 258

Note. The table lists the courses that participated in the electronic attendance monitoring system. The table shows the number of

students enrolled in the sections of courses using the electronic attendance monitoring system and the sections of courses that did not

use the system as well as presents the total numbers in the categories.

Table 7 provides the breakdown of the number of students enrolled in the sections

of courses using the electronic attendance monitoring system and the sections of courses

that did not use the system. In total, 640 students took part in course sections

implementing the electronic attendance monitoring system, and 953 students were in the

non-attendance monitoring course sections. The total number of students in the sample is

84

1593. Three courses participated in the electronic attendance monitoring system. The

History 101 course had 285 students enrolled in the attendance monitoring while 219

students were in the course sections that did not participate in attendance monitoring.

The enrollment for History 102 saw 291 students in the attendance monitoring course

sections and 540 students in the non-attendance monitoring course sections. Math 102

had 64 students in the course sections that participated in the attendance monitoring and

194 students in the course sections that did not participate.

The total number of students for each course is different for a number of reasons.

First, each course had different number of available course sections that could and did

participate. Two of the three History 101 class sections participated in the study. One of

the three class sections of History 102 participated in the study. Two of the seven class

sections of Math 102 course participated. Second, the size of the enrollment cap on the

class could have affected the number of students enrolled in each course section. For

instance, the number of students allowed to register in a course section of Math 102 is

lower than that of History 101 or History 102. Next, the number of course sections for

each class and the classroom assigned for the class section to meet was out of control of

the researcher as it is assigned at least two semesters prior to that semester. The

classroom assigned for a course section dictates how many students can enroll in the

class. Finally, faculty who voluntarily participated in the electronic attendance

monitoring system controlled the number of available students for attendance monitoring.

Table 8 presents the academic success data combining all participants in the

course sections in the Spring 2015 Semester. Table 8 presents the raw data of academic

success rates, students who received an A, B, or C end-of-term grade in the sections of

85

courses using the electronic attendance monitoring system and the sections of courses

that did not use the system.

Table 8

Academic Success in Participating Courses: Monitored vs. Non-Monitored

Courses

Attendance

Monitoring Number

(%)

Non-Attendance

Monitoring Number

(%)

History 101 - World Civilization I 161 (56.5%) 80 (36.5%)

History 102 - World Civilization II 205 (70.4%) 258 (47.8%)

Math 102 - Brief Applied Calculus 41 (64.1%) 95 (49.0%)

Note. The table lists the courses that participated in the electronic attendance monitoring system. The table compares the combined

academic success rates in the sections of courses using the electronic attendance monitoring system and the sections of courses that

did not use the system.

Table 8 presents the end-of-term academic success data for the three courses that

took part in the electronic attendance monitoring system as compared to the course

sections that did not participate. While other factors could have influenced the academic

success rates, they are not known at this time. However, the main difference between

these course sections is the implementation of the electronic attendance monitoring

system. The results show higher levels of academic success in the courses using the

electronic attendance monitoring system. The results for History 101 indicate that 56.5%

(n = 161) of students enrolled in the attendance monitored class sections were

academically successful as compared to 36.5% (n = 80) in the class sections that did not

participate. The data shows a 20.0% increase in academic success rates between the

attendance monitoring and non-attendance monitoring classes when using an electronic

attendance monitoring system. History 102 had the highest number of students in a class

that participated in the electronic attendance monitoring system. History 102 saw a

86

70.4% (n = 205) rate of academic success in attendance monitored classes as opposed to

47.8% (n = 258) in the non-attendance monitored classes. This represents a 22.6%

increase in the academic success rate between these classes. The results from Math 102

indicated an 25.1% increase in the academic success rates between the course sections.

The course sections that used attendance monitoring had a 64.1% (n = 41) academic

success rate versus 49.0% (n = 95) rate in the non-attendance monitoring classes.

The data has shown a difference in the academic success rates between the

sections of courses using the electronic attendance monitoring system and the sections of

courses that did not use the system. The end-of-term grades were provided by the Office

of Institutional Research in an anonymous fashion and because of that, descriptive

statistics can be used to see the actual differences in the end-of-term academic success

grades. Tables 9, 10, and 11 compare the actual course grades for History 101, History

102, and Math 102 during the Spring 2015 Semester in the sections of courses using the

electronic attendance monitoring system and the sections of courses that did not use the

system. Table 9 presents the information from History 101.

Table 9

Academic Success Course Data Comparison: History 101

Group

Success Not Success

A B C D F W

Attendance

Monitored

24

(8.4%)

67

(23.5%)

70

(24.6%)

35

(12.3%)

57

(20.0%)

32

(11.2%)

Non-Attendance

Monitored

13

(5.9%)

38

(17.4%)

29

(13.2%)

33

(15.1%)

71

(32.4%)

35

(16.0%)

Note. The table represents the total number of students enrolled in the course during the Spring 2015 semester and is separated into

two categories, attendance monitored and non-attendance monitored. The attendance monitored category represents course sections that participated in the electronic attendance monitoring system and has 285 participants. Conversely, the non-attendance monitored

category represents the sections of the course that did not participate in the electronic attendance monitoring system and has 265

participants total. The end-of-term grades of A, B, and C, are classified as “Success”. The end-of-term grades of D, F, and W are

classified as “Not Success.

87

Table 9 presents the academic success data of the sample and divides the data by

attendance monitored,” the class sections participating in the electronic attendance

monitoring system, and non-attendance monitored,” the class sections that did not take

part. The data in Table 9 shows academic success in this course is greater in the class

sections that implemented the electronic attendance monitoring system. As reported

earlier, the students enrolled in the History 101 class sections that implemented the

electronic attendance monitoring system had a 56.5% academic success rate as compared

to 36.5% in the class sections that did not implement the system. Specifically, as

compared to the courses that did not implement attendance monitoring, the courses that

did monitor for attendance saw an increase of 2.5% in A grades, an increase of 6.1% of B

grades, and an increase of 11.4% in C grades. Conversely, the data in the D, F, and W

columns (Not Success), shows a reduction in the number of students who are in each

column. The attendance monitored courses had a 2.8% drop in D grades, a 12.4% drop in

F grades, and a 4.8% drop in the Withdrawal rate. The data shows a difference is present

by implementing the electronic attendance monitoring system.

Table 10 presents the actual course grades for History 102 in the Spring 2015

Semester of the courses and sections using the electronic attendance monitoring system

and courses that did not participate.

88

Table 10

Academic Success Course Data Comparison: History 102

Group

Success Not Success

A B C D F W

Attendance

Monitored

44

(15.1%)

83

(28.5%)

78

(26.8%)

34

(11.7%)

39

(13.4%)

13

(4.5%)

Non-Attendance

Monitored

42

(7.8%)

114

(21.1%)

102

(18.9%)

90

(16.7%)

147

(27.2%)

45

(8.3%)

Note. The table represents the total number of students enrolled in the course during the Spring 2015 semester and is separated into

two categories, attendance monitored and non-attendance monitored. The attendance monitored category represents course sections

that participated in the electronic attendance monitoring system and has 291 participants. Conversely, the non-attendance monitored category represents the sections of the course that did not participate in the electronic attendance monitoring system and has 540

participants total. The table includes the individual grades for students enrolled in the sections of courses using the electronic

attendance monitoring system and the sections of courses that did not use the system. The grades provided are A, B, C, D, F, and W. The end-of-term grades of A, B, and C, are classified as “Success”. The end-of-term grades of D, F, and W are classified as “Not

Success.

Table 10 presents the data, divided by attendance monitored, the class sections

participating in the electronic attendance monitoring system, and non-attendance

monitored”, the class sections that did not take part. The data in Table 10 shows

academic success in this course is greater in the class sections that implemented the

electronic attendance monitoring system. The students enrolled in the class sections that

implemented the electronic attendance monitoring system had a 70.4% academic success

rate as compared to 47.8% in the class sections that did not implement the system. When

comparing the courses implementing attendance monitoring to the ones that did not, a

difference in the academic success grades is present. Increases in academic success were

noted with an increase of 7.3% in A grades, a 7.4% increase of B grades, and a 7.9%

increase in C grades. Additionally, a decrease is present in the grades classified as “Not

Success”. The results show a 5.0% decrease in D grades, a 13.8% decrease in F grades,

and a 3.8% decrease in the Withdrawal rate. The percentage increases and decreases

show that a difference in academic success rates is present through the implementation of

89

an electronic attendance monitoring system. Table 11 presents the actual course grades

for Math 102 in the Spring 2015 Semester in the sections of courses using the electronic

attendance monitoring system and the sections of courses that did not use the system.

Table 11

Academic Success Course Data Comparison: Math 102

Group

Success Not Success

A B C D F W

Attendance

Monitored

13

(20.3%)

17

(26.6%)

11

(17.2%)

7

(10.9%)

7

(10.9%)

9

(14.1%)

Non-Attendance

Monitored

38

(19.6%)

26

(13.4%)

31

(16.0%)

22

(11.3%)

27

(13.9%)

50

(25.8%)

Note. The table represents the total number of students enrolled in the course during the Spring 2015 semester and is separated into

two categories, attendance monitored and non-attendance monitored. The attendance monitored category represents course sections that participated in the electronic attendance monitoring system and has 64 participants. Conversely, the non-attendance monitored

category represents the sections of the course that did not participate in the electronic attendance monitoring system and has 194

participants total. The table includes the individual grades for students enrolled in the sections of courses using the electronic attendance monitoring system and the sections of courses that did not use the system. The grades provided are A, B, C, D, F, and W.

The end-of-term grades of A, B, and C, are classified as “Success”. The end-of-term grades of D, F, and W are classified as “Not

Success.

Table 11 presents the data, divided by attendance monitored, the class sections

participating in the electronic attendance monitoring system, and non-attendance

monitored, the class sections that did not take part. The data in Table 11 shows academic

success in this course is greater in the class sections that implemented the electronic

attendance monitoring system. The students enrolled in the class sections that

implemented the electronic attendance monitoring system had a 64.1% academic success

rate as compared to 49% in the class sections that did not implement the system. The

data shows an increase of 0.7% in A grades, a 13.2% increase in B grades, and a 1.2%

increase in C grades. Further, a decrease of 0.4% of D grades, a decrease of 3.0% of F

grades, and a 11.7% decrease in the Withdrawal rate. In Math 102, having a 11.7%

decrease in the Withdrawal rate is a major difference as more students remained enrolled

90

in the course until the end of the academic term. Appendix E through M presents

additional tables that further report the outcomes of academic success comparing to the

historical averages for the courses.

Research Objective 2 – Difference in academic success rates when using an electronic

attendance monitoring system

Research Objective 2 determines if a difference in academic success rates exists

in courses using an electronic attendance monitoring system. To analyze the data, the

researcher used a logistic regression analysis to determine the significance between the

summary data for the courses involved (Field, 2009; Gellman & Hill, 2007; Shadish et

al., 2002; Wong & Mason, 1985).

Research Objective 2 tests for significance of students during the Spring 2015

Semester in the sections of courses using the electronic attendance monitoring system and

the sections of courses that did not use the system, to determine whether electronic

attendance monitoring affects academic success rates. Tables 12, 13, and 14 present the

results of the statistical testing. In this testing, the independent variable is the rate of

undergraduate student attendance for the sample and the dependent variable is the end-of-

semester grade, which measures academic success. Using the IBM SPSS program, the

researcher categorized the different course sections to provide statistical evidence of the

impact of the electronic attendance monitoring system on each individual course.

91

Table 12

Attendance Monitoring Versus Non-Attendance Monitoring: History 101

Class Comparison B Wald SE df eB Sig.

Monitored vs. Non-Monitored .814 19.485 .184 1 2.256 .000

Constant -.552 15.497 .140 1 .576 .000

Note. Course refers to the specific course taking part in the electronic attendance monitoring system. The logistic coefficient (β) helps

determine the direction of the relationship (positive or negative) based upon the numeric value (positive or negative). The Wald chi square statistic assesses the significance to coefficients, measuring the ratio of the square of the regression coefficient to the square of

the standard error of the coefficient. S.E. stands for Standard Error and is calculated by taking the standard deviation divided by the

square root of the sample size; the Standard Error measures the accuracy of comparing a statistic to a population. The df refers to the Degrees of Freedom and stands for the number of values in the final calculation of the statistic that are free to vary without violating

any constraints. The eB symbol represents the odds ratio and is an alternative value given to interpret the coefficient. Sig represents

the significance or p value using a 95% confidence interval where results less than .05 are found to be considered significant.

Table 12 shows the results of a logistic regression using IBM’s SPSS program to

determine if the History 101 end-of-term academic success rates between the sections of

courses that used the electronic attendance monitoring system versus sections of courses

that did not was statistically significant. The results express the impact of implementing

the electronic attendance monitoring system in this course. In the SPSS output, the

Model chi-square statistic within the Omnibus Tests of Model Coefficients table. The

results of the test, using a 95% confidence rate, was determined to be significant with the

results of Χ2 (1) = 19.485, p < .001. The Cox and Snell R2 = .039 and the Nagelkerke R2

= .052. The results indicate that the electronic attendance monitoring system made a

statistically significant difference in the end-of-term academic success rates. The

outcome was that students enrolled in the attendance monitored courses were 2.256 times

more likely to receive an A, B, or C end-of-term grade than students who were in the

non-attendance monitored classes. This statement applies to the students who were

enrolled in these classes during the Spring 2015 Semester. In conclusion, the testing was

significant, indicating that students enrolled in the attendance monitoring classes were

more successful academically.

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Table 13

Attendance Monitoring Versus Non-Attendance Monitoring: History 102

Class Comparison B Wald SE df eB Sig.

Monitored vs. Non-Monitored .958 38.324 .155 1 2.605 .000

Constant -.089 1.066 .086 1 .915 .302

Note. Course refers to the specific course taking part in the electronic attendance monitoring system. The logistic coefficient (β) helps

determine the direction of the relationship (positive or negative) based upon the numeric value (positive or negative). The Wald chi square statistic assesses the significance to coefficients, measuring the ratio of the square of the regression coefficient to the square of

the standard error of the coefficient. S.E. stands for Standard Error and is calculated by taking the standard deviation divided by the

square root of the sample size; the Standard Error measures the accuracy of comparing a statistic to a population. The df refers to the Degrees of Freedom and stands for the number of values in the final calculation of the statistic that are free to vary without violating

any constraints. The eB symbol represents the odds ratio and is an alternative value given to interpret the coefficient. Sig represents

the significance or p value using a 95% confidence interval where results less than .05 are found to be considered significant.

A Logistic Regression was conducted on the History 102 course to assess if the

end-of-term academic success rates between the course sections that used the electronic

attendance monitoring system versus courses that did not was significant. The SPSS

output reports the Model chi-square statistic within the Omnibus Tests of Model

Coefficients table. The test used a 95% confidence rate and the results determined a

significant difference where Χ2 (1) = 38.324, p < .001. The Cox and Snell R2 = .047 and

the Nagelkerke R2 = .063. The results indicate that the electronic attendance monitoring

system made a statistically significant difference in the end-of-term academic success

rates. The outcome was that students enrolled in the attendance monitored courses were

2.605 times more likely to be academically successful than students who were in the

courses that did not implement the electronic attendance monitoring system. This

statement applies to the students enrolled in these classes during the Spring 2015

Semester. In conclusion, the testing was significant, indicating the impact of an

electronic attendance monitoring system and that students enrolled in the attendance

monitoring classes were more successful academically.

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Table 14

Attendance Monitoring Versus Non-Attendance Monitoring: Math 102

Class Comparison B Wald SE df eB Sig.

Monitored vs. Non-Monitored .619 4.334 .297 1 1.858 .037

Constant -.041 .82 .144 1 .960 .774

Note. Course refers to the specific course taking part in the electronic attendance monitoring system. The logistic coefficient (β) helps

determine the direction of the relationship (positive or negative) based upon the numeric value (positive or negative). The Wald chi square statistic assesses the significance to coefficients, measuring the ratio of the square of the regression coefficient to the square of

the standard error of the coefficient. S.E. stands for Standard Error and is calculated by taking the standard deviation divided by the

square root of the sample size; the Standard Error measures the accuracy of comparing a statistic to a population. The df refers to the Degrees of Freedom and stands for the number of values in the final calculation of the statistic that are free to vary without violating

any constraints. The eB symbol represents the odds ratio and is an alternative value given to interpret the coefficient. Sig represents

the significance or p value using a 95% confidence interval where results less than .05 are found to be considered significant.

Similar to the other courses, a Logistic Regression analysis assessed if the end-of-

term academic success rates for Math 102 were significantly different between the

sections of courses using the electronic attendance monitoring system and the sections of

courses that did not use the system. The Omnibus Tests of Model Coefficients used a

95% confidence rate and the results determined a significant difference between the

classes where Χ2 (1) = 4.334, p < .001. The Cox and Snell R2 = .017 and the Nagelkerke

R2 = .023. The results indicate that the electronic attendance monitoring system made a

significant difference in the end-of-term academic success rates. The outcome was that

students enrolled in the attendance monitored courses were 1.858 times more likely to be

academically successful than students who were in the courses that did not implement the

electronic attendance monitoring system. This statement applies to the students enrolled

in these classes during the Spring 2015 Semester. In conclusion, the testing was

significant, indicating the impact of an electronic attendance monitoring system and that

students enrolled in the attendance monitoring classes were more successful

academically.

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Research Objective 3 – Relationship between attendance rates and academic success

Research Objective 3 tested the relationship between undergraduate student

attendance rates and academic success in courses using an electronic attendance

monitoring system. The statistical testing used for this research objective is a point-

biserial test. The point-biserial test measures item reliability (Field, 2009). Point-biserial

tests assess the correlation of a dichotomous variable and a continuous variable (Field,

2009). For this test, the dichotomous variable is academic success, end-of-term grades of

an A, B, or C, or not successful academically, end-of-term grades of a D, F, or W. The

percentage of class that a student attended represents the continuous variable. In terms of

the importance for this research objective, the point-biserial test allows the researcher to

test whether the percentage classes that an undergraduate attended effects academic

success.

In Table 15, reports the results of the point-biserial statistical test. The results

present the information from running the point-biserial statistical test to determine the

relationship between undergraduate student attendance rates and academic success. The

information gathered from Research Objective 3 provides insight into the relationship

between undergraduate student attendance rates and academic success when using an

electronic attendance monitoring system.

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Table 15

Correlation Between Attendance Rates and Academic Success

Course

Pearson

Correlation Sig. (2-tailed) N

All Courses Combined .560 <.001 1593

History 101 World Civilization I .560 <.001 285

History 102 World Civilization II .549 <.001 291

Math 102 Brief Applied Calculus .540 .001 64

Table 15 presents the correlation between attendance rates and academic success.

The point-biserial test was formatted to combine all of the classes together into one data

set and allowed the courses to be split by the individual courses. Pearson coefficients can

result in either positive or negative relationships between the two variables. The Pearson

coefficient numbers can range from -1 to 1; where -1 represents a perfect negative

relationship and 1 represents a perfect positive relationship (Field, 2009; Statistic

Solutions, 2013). The Pearson coefficient number is squared to get the percentage of

variability in academic success that is accounted for by attendance in the classroom

(Field, 2009; Statistic Solutions, 2013). Stated another way, the stronger the correlation,

the more variability is explained. The correlations in Table 15 assess the correlations for

all courses, History 101, History 102, and Math 102. Each of the cases has a correlation

number above .540 which according to Statistic Solutions (2013), represents a strong

association between attendance and academic success.

The result of the test for the full sample indicated r (1593) = .560 p < .001 and the

r value of .560 indicates a strong correlation between attendance and academic success.

Further, by squaring the Pearson Correlation, we find that 31.4% of the variability in

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academic success is related to attendance. Next, the data was separated in SPSS to assess

the individual courses. In History 101, the result was r (285) = .560 p < .001, which

according to Statistics Solutions (2013) indicates a strong correlation between attendance

and academic success. In History 101, the results show that attendance accounts for a

31.4% variability in academic success. History 102, which had the largest sample size in

the electronic attendance monitoring system, had a result of r (291) = .549 p < .001. This

indicates that attendance accounts for a 30.1% variability in academic success. The

results for Math 102 were very similar to the other courses but had a result of r (64) =

.540 p = .001. This result indicates a 29.2% variability in academic success as it relates

to attendance.

Research Objective 4 – Relationship between demographic factors and attendance rates

on academic success rates

Research Objective 4 tested the relationship between the literature-based

undergraduate demographic factors and attendance rates on student academic success in

courses using an electronic attendance monitoring system. Research Objective 4 uses a

logistic regression analysis, which is used to determine the interaction that the

undergraduate student attendance rates and attendance based demographic factors have

on the academic success grades.

The researcher uses dummy variables for ethnicity due to the presence of three

distinct categories, White, Black, and other ethnicity. Due to the nature of the classes and

the non-random assignment that occurs, the dummy variables are used to allow a

breakdown of different ethnicities. The study uses three ethnicity categories because the

White category was a large majority of the sample, but the Black ethnicity had above

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35%. Each of the other ethnicities did not have a large enough percentage to allow for

individual testing to occur.

Introduction to Research Objective 4

In total, 1,593 students enrolled in courses included in this study. The independent

variables were attendance rates and demographic variables affecting attendance. Table

16 shows the identification coding that took place for the demographic variables.

Table 16

Logistic Regression Identification Coding Used in SPSS

Demographic Variable 0 1

Gender Female Male

Local Residence Commuter Resident

State Residence In-State Out-of-State

Greek Affiliation No Yes

Admit Type Freshman Transfer

Cumulative GPA Below 2.50 2.5 and Above

Age Traditional (18-24) Non-Traditional (>24)

Classification Underclass Upperclass

Ethnicity White

Black (1)

Other Ethnicity (2)

Enrollment Status Part-Time Full-Time

Attendance was used in the logistic regression and calculated by using the

percentage of attended class in the logistic regression. The researcher conducted a

Logistic regression and IBM’s SPSS program was used to determine if the full model of

variables against the constant only model was statistically significant.

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All Courses Combined

Table 17 presents data illustrating the interactions of the undergraduate student

attendance figures, demographics, and academic success rates. Table 17 presents the,

data using a Wald test to determine if the independent variables are significant (Field,

2009; Goodwin, 2009; Trochim, 2006). The variable(s) found not significant are deleted

from the predictive equation, as they do not influence the results (Field, 2009; Goodwin,

2009; Trochim, 2006). Table 17 also highlights the logistic regression coefficient and

odds ratio for each of the attendance based demographic variables. The researcher used a

p < .05 criterion for the significance value.

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Table 17

Demographic and Attendance Relationship on Academic Success – All Courses

Combined

Demographic Variable B Wald SE df eB Sig.

Gender -.366 2.174 .227 1 .694 .136

Local Residence .424 6.995 .264 1 1.528 .141

State Residence .104 .513 .280 1 1.110 .731

Greek Affiliation .923 7.135 .308 1 2.518 .005

Admit Type -.601 6.112 .351 1 .548 .123

Cumulative GPA 1.643 43.891 .248 1 5.171 .000

Age 1.594 9.324 .714 1 4.924 .026

Classification .855 4.844 .388 1 2.350 .028

Ethnicity 9.324 2 .009

White (0)

Black (1) -907 9.222 .299 1 .404 .002

Other Ethnicity (2) -.451 .960 .461 1 .637 .327

Enrollment Status .472 .311 .846 1 1.603 .577

Attendance .078 78.187 .009 1 1.081 .000

Constant -7.726 43.527 1.171 1 .000 .000

Note. The logistic coefficient (β) helps determine the direction of the relationship (positive or negative) based upon the numeric value

(positive or negative). The Wald chi square statistic assesses the significance to coefficients, measuring the ratio of the square of the regression coefficient to the square of the standard error of the coefficient. S.E. stands for Standard Error and is calculated by taking

the standard deviation divided by the square root of the sample size; the Standard Error measures the accuracy of comparing a statistic

to a population. The df refers to the Degrees of Freedom and stands for the number of values in the final calculation of the statistic that are free to vary without violating any constraints. The eB symbol represents the odds ratio and is an alternative value given to

interpret the coefficient. Sig represents the significance or p value using a 95% confidence interval where results less than .05 are

found to be considered significant.

The SPSS output included the Model chi-square statistic within the Omnibus

Tests of Model Coefficients table. The Omnibus test and Model chi-square statistic

determines if the model is a significant fit (Field, 2009; Shadish et al., 2002; Statistic

Solutions, 2013). To be considered a significant fit, the model must have a p value of

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less than .05 and the results show significance where Χ2 (11) = 312.172, p = .002. The

SPSS generated classification table provides the overall percentage of correctly predicted

cases (Field, 2009). The percentage for the null model was 63.6% which increased to

82.7% when using the full model data. The Hosmer and Lemeshow goodness-of-fit test

indicates that the model is a good fit for the data, Χ2 (11) = 8.701, p = .368 (Field, 2009;

Shadish et al., 2002; Statistic Solutions, 2013).

Table 17 presents the results of the logistic regression. In the results, the study

shows that of the demographic variables included in the analysis, Greek affiliation,

Cumulative GPA, Age, Classification, Ethnicity – White, Ethnicity – Black, and

Attendance are all significant with a p value less than .05. Because of finding

significance in these variables, their odds ratio number is able to be used. For Greek

affiliation, the students who were involved in Greek Life in this sample were found to be

2.518 times more successful than non-Greek Life students. Students with a Cumulative

GPA of 2.5 and Above were 5.171 times more likely to be academically successful than

students who had Below a 2.5 Cumulative GPA. Students who were of the Non-

Traditional Age, Above 24 years old, were found to be 4.924 times more successful than

Traditional Age students, 18-24 years old. In the Classification demographic, Upperclass

students were 2.350 times more likely to be successful as compared to Underclass

students. Ethnicity used dummy variables because the category had three different

subsets to analyze. Because of this, the White category, which had the largest percentage

of students, was used as the comparison group for ethnicity. The data used the White

category as the comparison and significance was found in the Ethnicity-Black category.

Because this result is inversely related to the White category, the results found that Black

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students are 0.404 times less likely to be successful than White students. Attendance was

found to be significant but used a continuous variable for assessment in the logistic

regression. As such, for each 1% increase in attendance, the odds of a person being

academically successful is 1.081 times more likely.

Gender, Local Residence, State Residence, Admit Type, and Enrollment Status

were not found to have significance in the logistic regression. By not finding

significance for these demographic variables, it means that the variable does not

contribute to the outcome of academic success among the participants. These factors

have been found in previous research to be important to academic success and

attendance. But this previous research only analyzed one variable at a time as opposed to

the current study which combined multiple demographic variables. The variables are not

able to be removed from the equation as it will affect the significance of the other

variables.

This section of Research Objective 4 details the results from the logistic

regression analysis. The data provided shows no indications of any influential cases or

outliers in the data. No evidence exists to suggest that the testing violated the

assumptions of a logistic regression test. In conclusion, the testing was statistically

significant.

History 101 Analysis

Next, Table, 18 shows the results of the logistic regression when the data is split

for only the History 101 (World Civilization I) course. Also included in Table 18 are the

logistic regression coefficient, Wald test, and odds ratio for each of the attendance based

demographic variables. The researcher used a .05 criterion for the significance value.

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Table 18

Demographic and Attendance Relationship on Academic Success – History 101

Demographic Variable B Wald SE df eB Sig.

Gender -.636 3.008 .367 1 .529 .083

Local Residence .189 .200 .422 1 1.208 .655

State Residence .202 .216 .435 1 1.224 .642

Greek Affiliation 1.299 6.889 .495 1 3.665 .009

Admit Type -.418 .461 .616 1 .658 .497

Cumulative GPA 1.263 12.092 .363 1 3.535 .001

Age 1.608 2.415 1.035 1 4.994 .120

Classification .878 2.040 .615 1 2.407 .153

Ethnicity 1.099 2 .577

White (0)

Black (1) -.455 1.095 .425 1 .641 .295

Other Ethnicity (2) -.420 .091 .644 1 .818 .763

Enrollment Status -.855 .364 1.417 1 .425 .546

Attendance .077 38.645 .012 1 1.080 .000

Constant -5.990 11.917 1.735 1 .003 .001

Note. The logistic coefficient (β) helps determine the direction of the relationship (positive or negative) based upon the numeric value

(positive or negative). The Wald chi square statistic assesses the significance to coefficients, measuring the ratio of the square of the regression coefficient to the square of the standard error of the coefficient. S.E. stands for Standard Error and is calculated by taking

the standard deviation divided by the square root of the sample size; the Standard Error measures the accuracy of comparing a statistic

to a population. The df refers to the Degrees of Freedom and stands for the number of values in the final calculation of the statistic that are free to vary without violating any constraints. The eB symbol represents the odds ratio and is an alternative value given to

interpret the coefficient. Sig represents the significance or p value using a 95% confidence interval where results less than .05 are

found to be considered significant.

The Omnibus Tests of Model Coefficients table and Model chi-square statistic

determines a significant fit as long as model has a p value of less than .05 (Field, 2009;

Shadish et al., 2002; Statistic Solutions, 2013). The results show significance where Χ2

(11) = 138.200, p = .001 (Field, 2009). The SPSS generated classification table provides

the overall percentage of correctly predicted cases (Field, 2009). The percentage for the

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null model was 56.5% which increased to 81.4% when using the full model data. The

Hosmer and Lemeshow goodness-of-fit test indicates that the model is a good fit for the

data, Χ2 (11) = 7.336, p = .501.

Table 18 presents the results of the logistic regression. The study shows the

demographic factors of Greek affiliation, Cumulative GPA, and Attendance were

significant as their p value was less than .05. Since these variables were found to be

significant, their odds ratio numbers are able to be used. In History 101, the study shows

that Greek students are 3.665 times more likely to succeed than non-Greek students and

students with a Cumulative GPA of 2.5 and above are 3.535 times more likely to succeed.

Attendance used a continuous variable and as such, for every 1% increase in attendance,

the odds of a person succeeding in the course was 1.080 times more likely to happen.

Gender, Local Residence, State Residence, Admit Type, Age, Classification,

Ethnicity, and Enrollment Status were not found to have significance in the logistic

regression. The lack of significance means that during this time, using this data, the

variable does not contribute to the outcome of academic success. In previous research,

individually, these variables have shown academic success and attendance. The variables

are not able to be removed from the equation as it will affect the significance of the other

variables.

This section of Research Objective 4 details the results from the logistic

regression analysis. The data provided shows no indications of any influential cases or

outliers in the data. No evidence exists to suggest that the testing violated the

assumptions of a logistic regression test. In conclusion, the testing was statistically

significant.

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History 102 Analysis

Next, Table, 19 shows the results of the logistic regression for only the History

102 (World Civilization II) course. Also included in Table 19 are the logistic regression

coefficient, Wald test, and odds ratio for each of the attendance based demographic

variables. The researcher used a .05 criterion for the significance value.

Table 19

Demographic and Attendance Relationship on Academic Success – History 102

Demographic Variable B Wald SE df eB Sig.

Gender -.034 .007 .410 1 .967 .934

Local Residence .500 1.165 .463 1 1.649 .280

State Residence -.007 .000 .486 1 .993 .988

Greek Affiliation .484 .749 .559 1 1.623 .387

Admit Type -.297 .192 .679 1 .743 .661

Cumulative GPA 1.946 22.434 .411 1 6.998 .000

Age .024 .000 1.461 1 1.024 .987

Classification 2.081 8.217 .726 1 8.009 .004

Ethnicity 12.751 2 .002

White (0)

Black (1) -1.856 12.377 .528 1 .156 .000

Other Ethnicity (2) -1.140 2.248 .760 1 .320 .134

Enrollment Status .1.194 .436 1.808 1 3.2993 .509

Attendance .087 27.876 .016 1 1.091 .000

Constant -9.408 16.510 2.315 1 .000 .000

Note. The logistic coefficient (β) helps determine the direction of the relationship (positive or negative) based upon the numeric value

(positive or negative). The Wald chi square statistic assesses the significance to coefficients, measuring the ratio of the square of the regression coefficient to the square of the standard error of the coefficient. S.E. stands for Standard Error and is calculated by taking

the standard deviation divided by the square root of the sample size; the Standard Error measures the accuracy of comparing a statistic

to a population. The df refers to the Degrees of Freedom and stands for the number of values in the final calculation of the statistic that are free to vary without violating any constraints. The eB symbol represents the odds ratio and is an alternative value given to

interpret the coefficient. Sig represents the significance or p value using a 95% confidence interval where results less than .05 are

found to be considered significant.

105

The Omnibus Tests of Model Coefficients table and Model chi-square statistic

determines a significant fit as long as model has a p value of less than .05 (Field, 2009;

Shadish et al., 2002; Statistic Solutions, 2013). The results show significance where Χ2

(11) = 146.756, p = .001 (Field, 2009). The SPSS generated classification table provides

the overall percentage of correctly predicted cases (Field, 2009). The percentage for the

null model was 56.5% which increased to 81.4% when using the full model data. The

Hosmer and Lemeshow goodness-of-fit test indicates that the model is a good fit for the

data, Χ2 (11) = 4.004, p = .857.

Table 19 presents the results of the logistic regression. The analysis shows which

demographic factors have significance, which are Cumulative GPA, Classification,

Ethnicity, and Attendance as their p value is less than .05. Because these variables are

significant, their odds ratio numbers are able to be used. The results indicate that

students with a Cumulative GPA of 2.5 and Above are 6.998 times more likely to be

academically successful than students with Below 2.5 GPA. Upperclass students are

8.009 times more likely to succeed in History 102 as compared to Underclass students.

Because Ethnicity has three subset categories, dummy variables had to be used to analyze

the data. The comparison category was White students and the analysis found

significance in both the comparison and Black subsets. Black students were found to be

.156 times less likely to be successful academically than the comparison group. The

reason for this is because the significance is inversely related to the comparison group.

Attendance used a continuous variable in the analysis and as such for each 1% increase in

attendance rates, a student was 1.091 times more likely to succeed academically.

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Gender, Local Residence, State Residence, Greek Affiliation, Admit Type, Age,

and Enrollment Status were not found to have significance in the logistic regression.

These variables do not contribute to the outcome of the logistic regression analysis

because they lack significance. The variables are not able to be removed from the

equation as it will affect the significance of the other variables.

This section of Research Objective 4 details the results from the logistic

regression analysis. The data provided shows no indications of any influential cases or

outliers in the data. No evidence exists to suggest that the testing violated the

assumptions of a logistic regression test. In conclusion, the testing was statistically

significant.

Math 102 Analysis

Next, Table, 20 shows the results of the logistic regression for only the Math 102

(Brief Applied Calculus) course. Also included in Table 20 are the logistic regression

coefficient, Wald test, and odds ratio for each of the attendance based demographic

variables. The researcher used a .05 criterion for the significance value.

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Table 20

Demographic and Attendance Relationship on Academic Success – Math 102

Demographic Variable B Wald SE df eB Sig.

Gender -2.514 2.257 1.713 1 .076 .133

Local Residence 1.955 1.298 1.716 1 7.063 .255

State Residence 1.933 1.119 1.827 1 6.098 .290

Greek Affiliation 1.702 1.163 1.578 1 5.484 .281

Admit Type -2.830 1.837 2.088 1 .059 .175

Cumulative GPA 6.150 5.597 2.600 1 468.920 .018

Age 4.637 3.082 2.641 1 103.224 .079

Classification 2.882 1.471 2.376 1 17.856 .225

Ethnicity .292 2 .864

White (0)

Black (1) -.224 .0152 1.826 1 1.199 .902

Other Ethnicity (2) 2.632 .282 4.959 1 113.905 .596

Enrollment Status .058 .001 2.128 1 1.060 .978

Attendance .174 3.882 .088 1 11.189 .049

Constant -18.260 4.107 9.010 1 .000 .043

Note. The logistic coefficient (β) helps determine the direction of the relationship (positive or negative) based upon the numeric value

(positive or negative). The Wald chi square statistic assesses the significance to coefficients, measuring the ratio of the square of the regression coefficient to the square of the standard error of the coefficient. S.E. stands for Standard Error and is calculated by taking

the standard deviation divided by the square root of the sample size; the Standard Error measures the accuracy of comparing a statistic

to a population. The df refers to the Degrees of Freedom and stands for the number of values in the final calculation of the statistic that are free to vary without violating any constraints. The eB symbol represents the odds ratio and is an alternative value given to

interpret the coefficient. Sig represents the significance or p value using a 95% confidence interval where results less than .05 are

found to be considered significant.

The Omnibus Tests of Model Coefficients table and Model chi-square statistic

determines a significant fit as long as model has a p value of less than .05 (Field, 2009;

Shadish et al., 2002; Statistic Solutions, 2013). The results show significance where Χ2

(11) = 44.644, p = .001). The SPSS classification table provides the overall percentage of

correctly predicted cases (Field, 2009; Statistic Solutions, 2013). The percentage for the

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null model was 64.1% which increased to 85.9% when using the full model data. The

Hosmer and Lemeshow goodness-of-fit test indicates that the model is a good fit for the

data, Χ2 (11) = 2.510, p = .961. Included in Table 20 are figures that show the

importance of the demographic variables.

Table 20 presents the results of the logistic regression. The analysis shows only

two areas of the logistic regression analysis have significance, Cumulative GPA and

Attendance. The logistic regression output shows that students with a Cumulative GPA

of 2.5 and Above are 468.920 times more likely to be academically successful than

students with Below 2.5 GPA. Attendance used a continuous variable in the analysis and

as such for each 1% increase in attendance rates, a student was 11.189 times more likely

to succeed academically.

Gender, Local Residence, State Residence, Greek Affiliation, Admit Type, Age,

Classification, Ethnicity, and Enrollment Status were not found to have significance in

the logistic regression. With the data for this sample, these variables are not shown to

contribute to the overall logistic regression analysis. Removing these variables would

adjust the outcome of the analysis and must not occur.

Research Objective 4 details the results from the logistic regression analysis. The

data provided shows no indications of any influential cases or outliers in the data. No

evidence exists to suggest the testing violated the assumptions of a logistic regression

test. The results showed that the testing was statistically significant.

Chapter Summary

The analysis indicates that when using the electronic attendance monitoring

system, a positive relationship between attendance and academic success exists.

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Furthermore, the findings present information on specific demographic variables

affecting attendance that shows significance in the courses participating in the electronic

attendance monitoring system. For each of the courses participating in the electronic

attendance monitoring system, attendance was found to impact academic success rates in

a statistically significant fashion. In Chapter V, the researcher presents the final

summary, conclusions, and recommendations of this study. Chapter V also includes the

limitations of the study and recommendations for further research.

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CHAPTER V – CONCLUSIONS

Generally, poor college student attendance is due to a lack of accountability,

which enables a low rate of attendance, while also limiting interactions between faculty

and students (Jones, Crandall, Vogler, & Robinson, 2013). Nationally, and in the state of

Mississippi, a skill gap is present in the workforce and one factor affecting this gap is the

low level of undergraduate degree completion (Altonji et al., 2012; Kaplan, 2017;

National Center for Education Statistics, 2017; Swanson & Holton, 2009; White House,

Office of the Press Secretary,2009). Automated accountability tools, like the electronic

attendance monitoring system, can assist in increasing interaction between faculty and

students, greater academic success, and increased graduation rates (Borland & Howsen,

1998). During this time, undergraduate students develop workplace skills that contribute

to the human capital needs of society (Blackwell, Bowes, Harvey, Hesketh, & Knight,

2001; Noel et al., 1985; Pascarella & Terenzini, 1991; Robertson et al., 2017).

The purpose of the present study is to determine the influence of an electronic

attendance monitoring system on undergraduate student success. Specifically, the study

assesses if an electronic attendance monitoring system affects student academic success.

The findings and conclusions drawn from the research are discussed throughout Chapter

V, followed by recommendations and suggestions for policy, practice, and future

research.

Findings, Conclusions, and Recommendations

The findings, conclusions, and recommendations outlined in the following

sections relate to the analysis from the analysis in Chapter 4 and the problem of the

present research. All research objectives were examined through the study and as a result

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of the analysis, certain outcomes were expected and in line with previous literature.

However, the analysis revealed results contradicting the literature providing additional

insight into the role of the electronic attendance monitoring system on academic success.

The findings, conclusions, and recommendations result from the data analysis.

Finding 1: Academic success rates increased with attendance monitoring

The results of this study show that students enrolled in the sections of courses

using the electronic attendance monitoring system were more successful academically

than the sections of courses that did not use the system. In each of the three courses,

History 101, History 102, and Math 102, a significant difference exists when comparing

the sections of courses that used the electronic attendance monitoring system to the

sections of courses that did not use the electronic attendance monitoring system. The

results indicate that implementing an electronic attendance monitoring system can

increase academic success for undergraduate students. These results may not always be

the outcome, but for this study, they were.

Conclusions

The findings of this study align with and support previous research that promotes

undergraduate student attendance accountability as a way to increase academic success

rates (Kuh et al., 2008; Moore, 2003; Noel et al., 1985). The lack of accountability

through attendance monitoring in classes can be detrimental to the learning aspirations of

students. While the lack of attendance accountability allows freedom of choice for

students to attend class, attendance monitoring systems promote attendance while

students experience learning atmospheres with multiple distractions (Gump, 2006;

Longhurst, 1999). Other universities have implemented electronic attendance monitoring

112

systems, but the results of their endeavors have not been published (Dicle & Levendis,

2013; Neman-Ford et al., 2008; Newsom, 2016; O’Connor, 2010). The present study

suggests the impact of implementing attendance accountability systems. While the

present study finds its basis in literature, the difference from the literature is the sample

participants and the difference in location. The present study replicates the underlying

notion that the presence of electronic attendance monitoring systems provides a way to

promote accountability among students.

Recommendations

Universities must implement systems aimed at promoting attendance

accountability. The following recommendations are predicated on the implementing a

system similar to the electronic attendance monitoring system used in this study. First,

universities can design outreach programs focused on students who are not attending

class. These programs may occur through a campus retention office, residence life office,

Greek Life office, or other major university departments that have large numbers of

student involvement. Next, by implementing an attendance accountability procedure

similar to the electronic attendance monitoring system, the opportunity exists for faculty

to communicate to students directly regarding their lack of attendance. Communication

originating from the faculty can lead to greater engagement between students and faculty.

Last, through the proper identification of student involvement in a student’s online

records, the opportunity exists for a faculty or staff member that the student knows, for

example through their on-campus involvement in co-curricular activities, to communicate

with them. Through all of these outreach options, the opportunity exists to communicate

the need for a behavior change to occur. Regardless of the method, higher education

113

institutions, already suffering from low retention and graduation rates, should provide

accountability structures that promote attendance which boosts undergraduate student

academic success.

Finding 2: Academic success increases as attendance increases

The results of the study indicate a positive relationship exists between attendance

and academic success. While other factors may impact academic success, this study

focused on the ability of attendance accountability to affect all students enrolled in the

sections of courses using the electronic attendance monitoring system while comparing

the outcomes to the sections of courses that did not use the system.

Conclusions

Previous research supports the importance of class attendance for academic

success. Students who do not attend class regularly may become disengaged with the

topic and lose the connection to the faculty member and the topic material (Blackwell et

al., 2001; Noel et al., 1985; Pascarella & Terenzini, 1991; Robertson et al., 2017).

Multiple reasons exist for students missing classes, but the most frequently stated reasons

include sickness, peer pressure, parental influence, work commitments, conflicts with

extracurricular activities, and the difficulty of their academic major (Longhurst, 1999).

The results for this finding shows a link between attendance and academic success in the

sample used during the present study.

Recommendations

The results of this study demonstrate the impact of electronic attendance

monitoring systems to address the lack of attendance accountablity in the classroom.

Strategies to assist students who do not attend class regularly be implemented. These

114

strategies can include the adoption of a university-wide attendance policy,

implementation of an electronic attendance monitoring system in courses with low

academic success rates, and marketing focused on the importance of classroom

attendance on academic success rates. Implementing attendance accountability programs

to promote academic success, especially in historically difficult courses, needs to occur at

universities. Additionally, the collection of data by the Office of Institutional Research

included the presence of an individual at the entrance to the classroom. Universities

should make all attempts to incorporate a person-to-person contact when collecting the

data when implementing the research study.

Finding 3: Courses are unique; Demographics correlate with academic success

differently in combined or individual data sets, while attendance remains important

throughout

In the request for data, The Office of Institutional Research identified the enrolled

course for each student. The data request included the demographic information of the

students. The data was analyzed in two distinct combinations; first analyzing all of the

data combining all courses in one data set and second analyzing each course using

separate data sets. The results of this research show the importance of certain

demographic factors to academic success. In the all courses combined data set, the

demographic factors of ethnicity, classification, age, cumulative GPA, and Greek

affiliation positively affected academic success of students using electronic attendance

monitoring systems. In History 101, the demographic factors of cumulative GPA, and

Greek affiliation have a positive effect on the academic success of students using

electronic attendance monitoring systems. In History 102, a positive effect exists among

115

the demographic factors of ethnicity, classification, and cumulative GPA on the academic

success of students using electronic attendance monitoring systems. In Math 102, a

positive effect is found between academic success and the demographic factor of

cumulative GPA of students using electronic attendance monitoring systems. By

combining the data all together and also separating the data by course, the analysis

reveals information regarding the importance of different factors on undergraduate

student success depending on the those included in the sample analysis. A significant

relationship between attendance and academic success remains present throughout the

analysis of the data, both combined and individually.

Conclusions

Previous research has shown that each of the demographic factors chosen for

analysis made a difference in attendance rates and academic success rates (Altermatt,

2007; Arredondo & Knight, 2005; Behar, 2010; Borland & Howsen, 1998; Caldas, 1993;

Chickering, 1974; Chimka et al., 2007; Lamdin, 1996; Lowis & Castley, 2008; Newman-

Ford et al., 2008; Romer, 1993; Stewart et al., 1985; Stewart & Rue, 1983; Yu et al.,

2010). However, the results of the present study, depending on if the combined data set

is used or if the data is separated by course, differs with the literature for some of the

demographic factors. Individually each class is unique and obtained different results for

each of the demographic factors. To explain the differences between previous literature

and the present results, differences exist in the population and sample, the types of

courses observed, the university where the study took place, and class size variations.

The most effective strategies that promote attendance accountability and academic

success involve implementing multiple strategies that recognize each course, class

116

section, and the uniqueness of students enrolled; what works for one course or class

section may not be successful in another course or class section.

The data analysis included the ability to determine factors from a combined data

set and from individual course specific data sets. The analysis allowed the researcher to

determine factors that remain constant and significant throughout the data sets for this

sample. Of the students utilizing the electronic attendance monitoring system, the

demographic factor of cumulative GPA effected academic success of students throughout

the analysis, regardless of analyzing the combined data or the course specific data. A

focus can be placed on cumulative GPA to lead students to academic success. Further, in

the sample combining all courses, the demographic factors of attendance, ethnicity,

classification, age, cumulative GPA, and Greek affiliation showed significance. The

results from the larger sample allow for a larger generalization to occur as compared to

the results from the individual courses. With the ability to include the largest amount of

students possible in the combined sample, outreach can occur to students using the data.

In the analysis of the data associated with this study, the importance of attendance

to academic success is demonstrated through the significance levels and agrees with

previous literature (Stanca, 2004; Thomas & Higbee, 2000; Vidler, 1980). Finding 2

surmises that the more a student attends class, the higher their academic grades will be at

the end of the term. In the analysis for Finding 3, attendance and the demographic factors

were analyzed concurrently, and the results show the significance of attendance to the

end of term academic success of students. The present study allows the researcher to

provide information illustrating that by using an electronic attendance monitoring system,

different courses achieve different results. Further, the present study shows the

117

uniqueness of the demographic factors of each student, but allows for the analysis of

multiple demographic factors at one time.

Recommendations

Faculty, staff, and students need to know why the electronic attendance

monitoring system is needed and to explain the results of the program. The academic

success of students is closely related to their ability to persist through to graduation.

Allowing stakeholders to use the data from the program allows individuals the

opportunity to make decisions to support their academic success.

An additional recommendation is for institutions to implement programs to assist

students who have a low cumulative GPA. Programs designed to assist students with a

low cumulative GPA support student retention and academic success (Singell & Wadell,

2010). Some examples of these programs are academic department organized tutoring

programs and academically focused skill building programs organized through the

campus retention office. Implementing this recommendation can assist students in

developing academic success skills needed to be successful in courses.

Another recommendation is for universities to look for undetected bias in the

curriculum for courses that could adversely affect different ethnic groups. Ethnicity is

shown to have significance in academic success when all the data is combined, but the

significance is different for the three categories of ethnicity in the analysis. Examining

for the presence of undetected bias serves as a tactic for universities to promote academic

success through the adaptation to the needs of students, while still promoting the

academic rigor needed to achieve a degree. Additionally, the data analysis demonstrates

that certain demographic factors contribute to a student’s academic success. The

118

information allows for the opportunity for outreach to occur to students in these

demographics. Furthermore, officials can seek to determine the underlying factors that

make students successful when they do have certain demographic factors.

A final recommendation lies with finding ways to utilize technology to assist

students that cannot attend class. Many different conflicts can occur affecting a student’s

ability to attend, but little is done to ensure that the student doesn’t miss out on the

pertinent information for the course. Universities should implement technological aids,

such as audio and video recordings, that assist students who are unable to attend class to

promote dissemination of information.

Implications for Human Capital Development

This study is important to three different audiences: the State, the institution, and

the individual. On a state level, improving attendance and graduation rates can positively

impact the state’s education level leading to increased economic prosperity among its

citizens (Adelman, 1999). Systemic change needs to occur in universities, which are

complex organizations, to witness success in improving the academic success, retention,

and graduation metrics. Research, over the past four decades, links the level of student

involvement or engagement with the more positive impact a student receives in the areas

of academic and workplace skill development (American College Testing, 2010; Bean &

Metzeler, 1985; Berger & Braxton, 1998; Gerdes & Mallinckrodt, 1994; Harrison, 2006;

Lowis & Castley, 2008; Pascarella & Terenzini, 2005; Tinto, 1993). States, including

Mississippi, have implemented laws linking higher education funding to academic

success, retention, and graduation data points (Institutions of Higher Learning Board,

State of Mississippi, 2013). On an institution level, recruitment and retention are key

119

aspects that affect a university’s student population. As student populations evolve,

institutions should implement innovative measures to promote and support student

success (Noel et al., 1985; The University of Southern Mississippi, 2014). Research

indicates identifying students at risk of dropping out, especially freshman, is the most

efficient way to boost retention and graduation rates, thus promoting academic success

(Reason, 2003). Institutions would be shortsighted to not implement interventions to

positively impact academic success (Mitchell et al., 2014; Quinton, 2016; Swanson &

Holton, 2009). From an individual standpoint, implementing attendance accountability

systems could be one way to assist students in achieving higher levels of education and

the development of workplace skills during college. The use of attendance monitoring

could increase performance in classes and graduation rates leading to more prepared

students for the workforce. In the state of Mississippi, only 29% of citizens have an

education level at or above an Associate’s degree (Education Commission of the States

2011). However, individuals who attain bachelor’s degree earn $300 more each week

than those that do not have a degree and experience a lower unemployment rate (Altonji

et al., 2012; National Center for Educational Statistics, 2005). As the education level of a

citizenry increases, so does the economic stability of the local economy. But, most

importantly, the increase in education level assists the individual in meeting the

qualifications for additional career opportunities.

Limitations

Researchers recognize limitations exist for all research. Limitations define the

parameters of a researcher’s scope of analysis (Roberts, 2010). Several limitations exist

in the present study. First, the study analyzes data from only one institution, The

120

University of Southern Mississippi, and studies only one of two campuses, the

Hattiesburg campus. The selection of the institution occurred due to its convenience and

record in the areas of retention and graduation rates. Another limitation of the study is

that the electronic attendance monitoring system was conducted for only one semester.

The research did not control for other interventions that might have occurred during the

time of the study, such as increased emphasis on study sessions or availability of

university-provided tutoring, among other possible interventions during the Spring 2015

Semester. The difference in enrolled students each semester may affect overall academic

success rates and influence the role demographics play on academic success rates.

Having a diverse student population for a course can assist in generalizing the study to

the university population and beyond. The study does not account for faculty-enforced

attendance policies. Undergraduate student attendance policies could be viewed as

having a non-effect on students’ success, as the policies have been in place for multiple

semesters prior to the electronic attendance monitoring system and may not have

influenced academic success rates.

Recommendations for Further Research

Opportunities arise for future research based upon the results, limitations, and

constraints of the research. Several suggestions for future research related to the topic of

electronic attendance monitoring systems follow:

• Implementation of a longitudinal study over multiple semesters. The study

could provide greater insight to promote academic success.

• Expand the research to a variety of other institutions. By doing this,

generalization of the research to a greater population could occur.

121

• Evaluate other literature-based demographic factors such as financial aid

status, first generation college student, academic major, academic college,

ACT/SAT score, incoming GPA, and disability accommodations.

• Assess if a difference occurs in active attendance monitoring versus passive

attendance monitoring in attendance rates and academic success rates.

• Determine ways to assess and monitor attendance in online only classes.

The areas for future research serve as recommendations for individuals interested in

implementing strategies to promote undergraduate student success. These additional

research areas could provide valuable information to universities, students, their families,

policy makers, and peer institutions.

Discussion

This quasi-experimental study using archival data allowed the researcher to gather

information about implementing an electronic attendance monitoring system. While

previous research was conducted on the topic, the study allowed for the researcher to gain

knowledge of a specific sample, at one point in time, at one university. Recent advances

in technology allow for better integration of systems to assist students in academic

success measures and add accountability in cost effective ways not addressed previously

by academic institutions.

The present study compared three courses with sections that implemented the

electronic attendance monitoring system and sections that did not. The findings showed

that a significant difference occurs in end-of-term grades and that the more a student

attends class, the better their academic success rate. Additionally, by analyzing

demographics simultaneously, information is available on the impact of certain

122

demographic factors. The results show the likelihood of academic success when

concurrently analyzing multiple demographic factors and attendance rates.

Entire university communities are engaged in finding ways to impact retention

and graduation rates. This study could help fill a gap in their knowledge and

understanding about attendance accountability. The results of this program should be

shared with faculty, staff, and students to explain why electronic attendance monitoring is

needed and explaining the results of the program.

Achieving academic success results from a combination of tactics, with each

tactic designed to assist certain members of the population. Attendance monitoring is not

the solution, but one solution. Nevertheless, the adoption of an electronic attendance

monitoring system can address attendance accountability issues in courses identified by

institutions through internal data analysis. The electronic attendance monitoring system

positively impacted the end-of-term grades of students in the sample. Increasing the end-

of-term grades of students is only one way to assist in retaining students and removing

barriers in an effort to boost graduation rates. Further, by taking steps to improve

attendance in the classroom, the development of workplace skills can occur as students

progress through college.

Summary

The purpose of this research was to determine the influence of an electronic

attendance monitoring system on undergraduate student success. Promoting student

development through interactions between faculty and students increases academic

success during the collegiate years and sets up a critical base for the future success of

individuals (Astin, 1993). This establishes a link between collegiate academic success

123

and human capital development. Four research objectives guided the research and

focused on the impact of an electronic attendance monitoring system on academic

success.

The study examined these research objectives using the data provided through

The Universityof Southern Mississippi’s Office of Institutional Research on the sections

of courses using the electronic attendance monitoring system and the sections of courses

that did not use the system. Appropriate measures to determine external factors that

could have impacted the research were identified, but which occurred naturally during the

system’s implementation. The study found a significant difference between the academic

success rates in the sections of courses using the electronic attendance monitoring system

and the sections of courses that did not use the system. Further, the study identified

demographic factors that play a part in undergraduate student academic success.

Attaining higher levels of education assists individuals with finding gainful

employment and leads to ecnomic stability (Bureau of Labor Statistics, 2015). With the

everchanging landscape of higher education, colleges and universities should implement

interventions designed to support undergraduate academic success and interaction with

faculty as they develop human capital (Institutions of Higher Learning Board, State of

Mississippi, 2013; Mitchell et al., 2014; Quinton, 2016). Implementing attendance

accountability measures, such as an electronic attendance monitoring system, represents

one stragety to positively impact academic success in college. Electornic attendance

monitoring provides a viable way to assist students and promote academic success.

124

APPENDIX A – Use of table permission

125

APPENDIX B – Institutional research pre-approval

126

APPENDIX C – Institutional review board approval letter

127

APPENDIX D – Attendance rates for all courses

Appendix D presents the electronic attendance monitoring data between the

courses as found in Figure 6 which has been adapted into this table for the purposes of

discussion.

Table A1.

Attendance Rates for All Courses Adapted from Figure 6

Attendance

Rate

History 101

World Civilization I

n (%)

History 102

World Civilization II

n (%)

Math 102

Brief Applied Calculus

n (%)

0-20% 6 (2.1%) 3 (1.2%) 4 (6.3%)

20.1-40% 20 (7.0%) 13 (4.4%) 1 (1.5%)

40.1-60% 34 (11.9%) 12 (4.1%) 4 (6.3%)

60.1-80% 47 (16.5%) 40 (13.7%) 9 (14.1%)

80.1-90% 71 (25.0%) 88 (30.2%) 11 (17.3%)

90.1-95% 49 (17.2%) 66 (22.7%) 14 (21.8%)

95.1-99.9% 39 (13.6%) 29 (10.0%) 9 (14.1%)

100% 19 (6.7%) 40 (13.7%) 12 (18.6%)

128

APPENDIX E – Academic success outcomes

The tables included in Appendix E present comparison tables presenting the

results from the Spring 2015 Semester of the classes that participated in the electronic

attendance monitoring system, classes that did not participate, and the historical average

for that class from fall 2009 through fall 2014.

Table A2.

History 101 Academic Success Outcomes

Table A3.

History 102 Academic Success Outcomes

History 102

World Civilization II Success Rate Not-Success Rate Withdrawal Rate

Participating Class Sections 70.4% 29.6% 4.5%

Non-Participating Class

Sections 47.8% 52.2% 8.3%

Historical Average 62.3% 37.7% 3.3%

History 101

World Civilization I Success Rate Not-Success Rate Withdrawal Rate

Participating Class Sections 56.5% 43.5% 11.2%

Non-Participating Class

Sections 36.5% 63.5% 16.0%

Historical Average 54.6% 45.5% 3.9%

129

Table A4.

Math 102 Academic Success Outcomes

Math 102

Brief Applied Calculus Success Rate Not-Success Rate Withdrawal Rate

Participating Class Sections 64.1% 35.9% 14.4%

Non-Participating Class

Sections 49.0% 51.0% 25.8%

Historical Average 58.8% 41.2% 4.5%

130

APPENDIX F – Electronic attendance monitoring system gender comparison results

The table in Appendix F presents a comparison of academic success rates for one

demographic factor showing the impact the electronic attendance monitoring system has

on academic success.

Table A5.

Comparison of Gender Results

Gender

Success

n (%)

Not Success

n (%)

Monitored

Female 259 (66.8%) 129 (33.2%)

Male 148 (58.7%) 104 (41.3%)

Total 407 (63.6%) 233 (36.4%)

Not Monitored

Female 292 (47.9%) 318 (52.1%)

Male 141 (41.1%) 202 (58.9%)

Total 433 (45.4%) 520 (54.6%)

131

APPENDIX G – Electronic attendance monitoring system local residence comparison

The table in Appendix G presents a comparison of academic success rates for one

demographic factor showing the impact the electronic attendance monitoring system has

on academic success.

Table A6.

Comparison of Local Residence Results

Local Residence

Success

n (%)

Not Success

n (%)

Monitored

Commuter 135 (52.5%) 122 (47.5%)

Resident 272 (71.0%) 111 (29.0%)

Total 407 (63.6%) 233 (36.4%)

Not Monitored

Commuter 258 (43.5%) 335 (56.5%)

Resident 175 (48.6%) 185 (51.4%)

Total 433 (45.4%) 520 (54.6%)

132

APPENDIX H – Electronic attendance monitoring system state residence comparison

The table in Appendix H presents a comparison of academic success rates for one

demographic factor showing the impact the electronic attendance monitoring system has

on academic success.

Table A7.

Comparison of State Residence Results

State Residence

Success

n (%)

Not Success

n (%)

Monitored

In-State 279 (59.9%) 187 (40.1%)

Out-of-State 128 (73.6%) 46 (26.4%)

Total 407 (63.6%) 233 (36.4%)

Not Monitored

In-State 341 (43.5%) 443 (56.5%)

Out-of-State 92 (54.4%) 77 (45.6%)

Total 433 (45.4%) 520 (54.6%)

133

APPENDIX I – Electronic attendance monitoring system Greek life affiliation

comparison

The table in Appendix I presents a comparison of academic success rates for one

demographic factor showing the impact the electronic attendance monitoring system has

on academic success.

Table A8.

Comparison of Greek Life Affiliation Results

Greek Affiliation

Success

n (%)

Not Success

n (%)

Monitored

No 258 (57.2%) 193 (42.8%)

Yes 149 (78.8%) 40 (21.2%)

Total 407 (63.6%) 233 (36.4%)

Not Monitored

No 328 (42.6%) 442 (57.4%)

Yes 105 (57.4%) 78 (42.6%)

Total 433 (45.4%) 520 (54.6%)

134

APPENDIX J – Electronic attendance monitoring system admit type comparison

The table in Appendix J presents a comparison of academic success rates for one

demographic factor showing the impact the electronic attendance monitoring system has

on academic success.

Table A9.

Comparison of Admission Type Results

Admit Type

Success

n (%)

Not Success

n (%)

Monitored

Freshman 355 (66.5%) 179 (33.5%)

Transfer 52 (49.1%) 54 (50.9%)

Total 407 (63.6%) 233 (36.4%)

Not Monitored

Freshman 290 (49.2%) 300 (50.8%)

Transfer 143 (39.4%) 220 (60.6%)

Total 433 (45.4%) 520 (54.6%)

135

APPENDIX K – Electronic attendance monitoring system cumulative GPA comparison

The table in Appendix K presents a comparison of academic success rates for one

demographic factor showing the impact the electronic attendance monitoring system has

on academic success.

Table A10.

Comparison of Cumulative GPA Results

Cumulative GPA

Success

n (%)

Not Success

n (%)

Monitored

Below 2.50 67 (31.8%) 144 (68.2%)

2.50 and Above 329 (82.9%) 68 (17.1%)

Total 407 (63.6%) 233 (36.4%)

Not Monitored

Below 2.50 102 (27.3%) 271 (72.7%)

2.50 and Above 284 (60.4%) 186 (39.6%)

Total 433 (45.4%) 520 (54.6%)

136

APPENDIX L – Electronic attendance monitoring system age comparison

The table in Appendix L presents a comparison of academic success rates for one

demographic factor showing the impact the electronic attendance monitoring system has

on academic success.

Table A11.

Comparison of Age Results

Age

Success

n (%)

Not Success

n (%)

Monitored

Traditional (18-24) 386 (63.4%) 223 (36.6%)

Non-Traditional (>24) 21 (67.7%) 10 (32.3%)

Total 407 (63.6%) 233 (36.4%)

Not Monitored

Traditional (18-24) 353 (46.1%) 412 (53.9%)

Non-Traditional (>24) 80 (42.6%) 108 (57.4%)

Total 433 (45.4%) 520 (54.6%)

137

APPENDIX M – Electronic attendance monitoring system classification comparison

The table in Appendix M presents a comparison of academic success rates for one

demographic factor showing the impact the electronic attendance monitoring system has

on academic success.

Table A12.

Comparison of Classification Results

Classification

Success

n (%)

Not Success

n (%)

Monitored

Underclass 336 (64.5%) 185 (35.5%)

Upperclass 71 (59.7%) 48 (40.3%)

Total 407 (63.6%) 233 (36.4%)

Not Monitored

Underclass 290 (49.2%) 299 (50.8%)

Upperclass 143 (39.3%) 221 (60.7%)

Total 433 (45.4%) 520 (54.6%)

138

APPENDIX N – Electronic attendance monitoring system ethnicity comparison

The table in Appendix N presents a comparison of academic success rates for one

demographic factor showing the impact the electronic attendance monitoring system has

on academic success.

Table A13.

Comparison of Ethnicity Results

Ethnicity

Success

n (%)

Not Success

n (%)

Monitored

White 264 (74.4%) 91 (25.6%)

Black 111 (48.3%) 119 (51.7%)

Other Ethnicity 32 (58.1%) 23 (41.8%)

Total 407 (63.6%) 233 (36.4%)

Not Monitored

White 256 (52.1%) (47.9%)

Black 134 (35.4%) 244 (64.6%)

Other Ethnicity 43 (53.0%) 41 (47.0%)

Total 433 (45.4%) 520 (54.6%)

139

APPENDIX O – Electronic attendance monitoring system enrollment status comparison

The table in Appendix O presents a comparison of academic success rates for one

demographic factor showing the impact the electronic attendance monitoring system has

on academic success.

Table A14.

Comparison of Enrollment Status Results

Enrollment Status

Success

n (%)

Not Success

n (%)

Monitored

Part-Time 6 (40.0%) 9 (60.0%)

Full-Time 401 (64.2%) 224 (35.8%)

Total 407 (63.6%) 233 (36.4%)

Not Monitored

Part-Time 40 (48.8%) 42 (51.2%)

Full-Time 393 (45.1%) 478 (54.9%)

Total 433 (45.4%) 520 (54.6%)

140

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